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Journal articles on the topic 'Computer-aided lung cancer detection system'

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Published: 30 May 2022
Last updated: 31 May 2022

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1

Ziyad, Shabana Rasheed, Venkatachalam Radha, and Thavavel Vayyapuri. "Overview of Computer Aided Detection and Computer Aided Diagnosis Systems for Lung Nodule Detection in Computed Tomography." Current Medical Imaging Formerly Current Medical Imaging Reviews 16, no. 1 (January 6, 2020): 16–26. http://dx.doi.org/10.2174/1573405615666190206153321.

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Background: Lung cancer has become a major cause of cancer-related deaths. Detection of potentially malignant lung nodules is essential for the early diagnosis and clinical management of lung cancer. In clinical practice, the interpretation of Computed Tomography (CT) images is challenging for radiologists due to a large number of cases. There is a high rate of false positives in the manual findings. Computer aided detection system (CAD) and computer aided diagnosis systems (CADx) enhance the radiologists in accurately delineating the lung nodules. Objectives: The objective is to analyze CAD and CADx systems for lung nodule detection. It is necessary to review the various techniques followed in CAD and CADx systems proposed and implemented by various research persons. This study aims at analyzing the recent application of various concepts in computer science to each stage of CAD and CADx. Methods: This review paper is special in its own kind because it analyses the various techniques proposed by different eminent researchers in noise removal, contrast enhancement, thorax removal, lung segmentation, bone suppression, segmentation of trachea, classification of nodule and nonnodule and final classification of benign and malignant nodules. Results: A comparison of the performance of different techniques implemented by various researchers for the classification of nodule and non-nodule has been tabulated in the paper. Conclusion: The findings of this review paper will definitely prove to be useful to the research community working on automation of lung nodule detection.
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Taher, Fatma, Naoufel Werghi, and Hussain Al-Ahmad. "Computer Aided Diagnosis System for Early Lung Cancer Detection." Algorithms 8, no. 4 (November 20, 2015): 1088–110. http://dx.doi.org/10.3390/a8041088.

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Amin, Javeria, Muhammad Sharif, and Mussarat Yasmin. "Computer Aided Diagnosis Systems for Lung Cancer Detection." Immunology‚ Endocrine & Metabolic Agents in Medicinal Chemistry 16, no. 999 (October 14, 2016): 1. http://dx.doi.org/10.2174/1871522216666161014160132.

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4

El-Baz, Ayman, Garth M. Beache, Georgy Gimel'farb, Kenji Suzuki, Kazunori Okada, Ahmed Elnakib, Ahmed Soliman, and Behnoush Abdollahi. "Computer-Aided Diagnosis Systems for Lung Cancer: Challenges and Methodologies." International Journal of Biomedical Imaging 2013 (2013): 1–46. http://dx.doi.org/10.1155/2013/942353.

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This paper overviews one of the most important, interesting, and challenging problems in oncology, the problem of lung cancer diagnosis. Developing an effectivecomputer-aided diagnosis(CAD) system for lung cancer is of great clinical importance and can increase the patient’s chance of survival. For this reason, CAD systems for lung cancer have been investigated in a huge number of research studies. A typical CAD system for lung cancer diagnosis is composed of four main processing steps: segmentation of the lung fields, detection of nodules inside the lung fields, segmentation of the detected nodules, and diagnosis of the nodules as benign or malignant. This paper overviews the current state-of-the-art techniques that have been developed to implement each of these CAD processing steps. For each technique, various aspects of technical issues, implemented methodologies, training and testing databases, and validation methods, as well as achieved performances, are described. In addition, the paper addresses several challenges that researchers face in each implementation step and outlines the strengths and drawbacks of the existing approaches for lung cancer CAD systems.
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Chapaliuk, Bohdan V., and Yuriy P. Zaychenko. "Recurrent neural network usage for computer-aided lung cancer detection system." System research and information technologies, no. 3 (October 7, 2019): 33–40. http://dx.doi.org/10.20535/srit.2308-8893.2019.3.03.

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6

Lo, Pechin, Matthew S. Brown, Jonathan Goldin, Eran Barnoy, Hyun J. Kim, Michael F. McNitt-Gray, and Denise R. Aberle. "Computer-aided lung cancer screening with CT: A clinically usable nodule detection and assessment system." Journal of Clinical Oncology 31, no. 15_suppl (May 20, 2013): 7562. http://dx.doi.org/10.1200/jco.2013.31.15_suppl.7562.

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7562 Background: The National Lung Screening Trial (NLST) recently demonstrated that lung cancer screening with low-dose CT reduces mortality. Current protocols use 4–8 mm nodules as positive screens. While there are some computer-aided nodule detection (CAD) systems currently available, they are rarely used in clinical practice because they generate too many false positives and lack reliable measurement tools. The purpose of this work is to develop a new CAD system to overcome these limitations and evaluate it against an expert panel of radiologists. Methods: The CAD system developed for lung nodule detection and measurement incorporates computer vision techniques including intensity thresholding, Euclidean Distance Transformation, and watershed segmentation. Rules pertaining to volume and shape were applied to automatically discriminate between nodules and bronchovascular anatomy. CAD system performance was assessed using 108 consecutive cases from the publically available Lung Imaging Database Consortium (LIDC), in which four radiologists reviewed each case. CT slice thickness ranged from 0.6–3.0 mm. Nodules were included that were: (a) ≥ 4mm, and (b) marked by a majority of the LIDC readers, and (c) ≥ 4 x CT slice thickness (to ensure adequate spatial resolution). Results: 44 of 108 subjects had one or more nodules meeting criteria. Median CAD sensitivity per subject for these 44 cases is reported for all nodules ≥ 4mm and the subset of nodules ≥ 8mm. The false positive (FP) rate per subject is reported for all 108 cases. The overall concordance correlation coefficient (CCC) between the CAD volume of each nodule and the LIDC reference volume was measured. Conclusions: Based on clinical CT screening protocols, a CAD system has been developed with high nodule sensitivity and a much lower false positive rate than previously reported systems. Automated volume measurements show strong agreement with the reference standard, providing a comprehensive detection and assessment workflow for lung cancer screening. [Table: see text]
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7

Qu, Ming Zhi, and Gui Rong Weng. "Lung Nodule Segmentation Using Mathematical Morphology." Applied Mechanics and Materials 58-60 (June 2011): 1378–83. http://dx.doi.org/10.4028/www.scientific.net/amm.58-60.1378.

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Contemporary computed tomography (CT) technology offers the better potential of screening for the early detection of lung cancer than the traditional x-ray chest radiographs. In order to help improve radiologists’ diagnostic performance and efficiency, many researchers propose to develop computer-aided detection and diagnosis (CAD) system for the detection and characterization of lung nodules depicted on CT images and to evaluate its potentially clinical utility in assisting radiologists. Based on review of computer-aided detection and diagnosis of lung nodules using CT at home and abroad in recent years, this paper presented a new algorithm that achieves an automated way for applying multi-scale nodule enhancement, mathematical morphology and morphological Segmentation.
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8

Wang, Hui, Yanying Li, Shanshan Liu, and Xianwen Yue. "Design Computer-Aided Diagnosis System Based on Chest CT Evaluation of Pulmonary Nodules." Computational and Mathematical Methods in Medicine 2022 (January 10, 2022): 1–12. http://dx.doi.org/10.1155/2022/7729524.

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At present, the diagnosis and treatment of lung cancer have always been one of the research hotspots in the medical field. Early diagnosis and treatment of this disease are necessary means to improve the survival rate of lung cancer patients and reduce their mortality. The introduction of computer-aided diagnosis technology can easily, quickly, and accurately identify the lung nodule area as an imaging feature of early lung cancer for the clinical diagnosis of lung cancer and is helpful for the quantitative analysis of the characteristics of lung nodules and is useful for distinguishing benign and malignant lung nodules. Growth provides an objective diagnostic reference standard. This paper studies ITK and VTK toolkits and builds a system platform with MFC. By studying the process of doctors diagnosing lung nodules, the whole system is divided into seven modules: suspected lung shadow detection, image display and image annotation, and interaction. The system passes through the entire lung nodule auxiliary diagnosis process and obtains the number of nodules, the number of malignant nodules, and the number of false positives in each set of lung CT images to analyze the performance of the auxiliary diagnosis system. In this paper, a lung region segmentation method is proposed, which makes use of the obvious differences between the lung parenchyma and other human tissues connected with it, as well as the position relationship and shape characteristics of each human tissue in the image. Experiments are carried out to solve the problems of lung boundary, inaccurate segmentation of lung wall, and depression caused by noise and pleural nodule adhesion. Experiments show that there are 2316 CT images in 8 sets of images of different patients, and the number of nodules is 56. A total of 49 nodules were detected by the system, 7 were missed, and the detection rate was 87.5%. A total of 64 false-positive nodules were detected, with an average of 8 per set of images. This shows that the system is effective for CT images of different devices, pixel pitch, and slice pitch and has high sensitivity, which can provide doctors with good advice.
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R., Sudha, and Umamaheswari K.M. "A Comprehensive Study of Deep Learning Approaches for Lung Nodule Analysis with Recent Computational Techniques." Webology 19, no. 1 (January 20, 2022): 749–63. http://dx.doi.org/10.14704/web/v19i1/web19053.

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Lung nodules resemble a spot or coin lesion, which is an abnormal part of the Lung. The size of the nodule is more significant than 3 cm, which may lead to cancer later. Lung cancer is one of the life treating cancers in the world. The American Lung Association says that the five years of survival rate is 18.6% lower when compared to other dominant cancers. But when it is diagnosed earlier, the survival rate can be increased by about 60%. Deep Learning-based Computer-aided Detection (CADe) and Computer-aided Diagnose (CADx) systems help the radiologist detect and classify the nodules as early as possible. This survey focuses on various methods, techniques, and algorithms available for Detecting, Classifying and Reducing the FP on Lung nodules. And also the familiar datasets that are used for processing the images. This work also reviews how the CNN model can be deployed and stored as cloud services.
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10

Gomathi. "A Computer Aided Diagnosis System for Lung Cancer Detection \Using Support Vector Machine." American Journal of Applied Sciences 7, no. 12 (December 1, 2010): 1532–38. http://dx.doi.org/10.3844/ajassp.2010.1532.1538.

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11

Dandıl, Emre. "A Computer-Aided Pipeline for Automatic Lung Cancer Classification on Computed Tomography Scans." Journal of Healthcare Engineering 2018 (November 1, 2018): 1–12. http://dx.doi.org/10.1155/2018/9409267.

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Lung cancer is one of the most common cancer types. For the survival of the patient, early detection of lung cancer with the best treatment method is crucial. In this study, we propose a novel computer-aided pipeline on computed tomography (CT) scans for early diagnosis of lung cancer thanks to the classification of benign and malignant nodules. The proposed pipeline is composed of four stages. In preprocessing steps, CT images are enhanced, and lung volumes are extracted from the image with the help of a novel method called lung volume extraction method (LUVEM). The significance of the proposed pipeline is using LUVEM for extracting lung region. In nodule detection stage, candidate nodules are determined according to the circular Hough transform- (CHT-) based method. Then, lung nodules are segmented with self-organizing maps (SOM). In feature computation stage, intensity, shape, texture, energy, and combined features are used for feature extraction, and principal component analysis (PCA) is used for feature reduction step. In the final stage, probabilistic neural network (PNN) classifies benign and malign nodules. According to the experiments performed on our dataset, the proposed pipeline system can classify benign and malign nodules with 95.91% accuracy, 97.42% sensitivity, and 94.24% specificity. Even in cases of small-sized nodules (3–10 mm), the proposed system can determine the nodule type with 94.68% accuracy.
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Mahmoodi, Maryam Sadat, and Seyed Abbas Mahmoodi. "Design of CAD System of Solitary Pulmonary Nodule with Harmony Classification and Fuzzy System." Medical Technologies Journal 1, no. 4 (November 29, 2017): 102. http://dx.doi.org/10.26415/2572-004x-vol1iss4p102-102.

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Introduction: Lung cancer is the most wide spread from of cancer, with the highest mortality rate worldwide. In this study, a computer-aided detection (CAD) system was developed for lung nodule detection, segmentation and recognition using CT images. So, we use a highly accurate supervised that uses lung images with the aim of assisting physicians in early detection of lung cancer. Methods: First, we segmented the lung area by masking techniques to isolated nodules and determined region of interest. Then, 24 features were extracted from images that included morphological, statistical and histogram. Important features were derived from the images for their posterior analysis with the aid of a harmony search algorithm and fuzzy systems. Results: In order to evaluate the performance of the proposed method, we used the LIDC database. the number of images included a database of 97 images whom 47 were diagnosed with lung cancer. Results of the base method show a sensitivity of 93%. Conclusion: The harmony search algorithm is optimized using fuzzy system for classification. The CAD system provides 93.1% accuracy.
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Sari, Sekar, Indah Soesanti, and Noor Akhmad Setiawan. "Development of CAD System for Automatic Lung Nodule Detection: A Review." BIO Web of Conferences 41 (2021): 04001. http://dx.doi.org/10.1051/bioconf/20214104001.

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Lung cancer is a type of cancer that spreads rapidly and is the leading cause of mortality globally. The Computer-Aided Detection (CAD) system for automatic lung cancer detection has a significant influence on human survival. In this article, we report the summary of relevant literature on CAD systems for lung cancer detection. The CAD system includes preprocessing techniques, segmentation, lung nodule detection, and false-positive reduction with feature extraction. In evaluating some of the work on this topic, we used a search of selected literature, the dataset used for method validation, the number of cases, the image size, several techniques in nodule detection, feature extraction, sensitivity, and false-positive rates. The best performance CAD systems of our analysis results show the sensitivity value is high with low false positives and other parameters for lung nodule detection. Furthermore, it also uses a large dataset, so the further systems have improved accuracy and precision in detection. CNN is the best lung nodule detection method and need to develop, it is preferable because this method has witnessed various growth in recent years and has yielded impressive outcomes. We hope this article will help professional researchers and radiologists in developing CAD systems for lung cancer detection.
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Elnakib, Ahmed, Hanan M. Amer, and Fatma E.Z. Abou-Chadi. "Early Lung Cancer Detection using Deep Learning Optimization." International Journal of Online and Biomedical Engineering (iJOE) 16, no. 06 (May 28, 2020): 82. http://dx.doi.org/10.3991/ijoe.v16i06.13657.

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This paper proposes a Computer Aided Detection (CADe) system for early detection of lung nodules from low dose computed tomography (LDCT) images. The proposed system initially pre-process the raw data to improve the contrast of the low dose images. Compact deep learning features are then extracted by investigating different deep learning architectures, including Alex, VGG16, and VGG19 networks. To optimize the extracted set of features, a genetic algorithm (GA) is trained to select the most relevant features for early detection. Finally, different types of classifiers are tested in order to accurately detect the lung nodules. The system is tested on 320 LDCT images from 50 different subjects, using an online public lung database, i.e., the International Early Lung Cancer Action Project, I-ELCAP. The proposed system, using VGG19 architecture and SVM classifier, achieves the best detection accuracy of 96.25%, sensitivity of 97.5%, and specificity of 95%. Compared to other state-of-the-art methods, the proposed system shows a promising results.
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ullah, Mati, Mehwish Bari, Adeel Ahmed, and Sajid Naveed. "Lungs Cancer Detection Using Digital Image Processing Techniques: A Review." Mehran University Research Journal of Engineering and Technology 38, no. 2 (April 1, 2019): 351–60. http://dx.doi.org/10.22581/muet1982.1902.10.

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From last decade, lung cancer become sign of fear among the people all over the world. As a result, many countries generate funds and give invitation to many scholars to overcome on this disease. Many researchers proposed many solutions and challenges of different phases of computer aided system to detect the lung cancer in early stages and give the facts about the lung cancer. CV (Computer Vision) play vital role to prevent lung cancer. Since image processing is necessary for computer vision, further in medical image processing there are many technical steps which are necessary to improve the performance of medical diagnostic machines. Without such steps programmer is unable to achieve accuracy given by another author using specific algorithm or technique. In this paper we highlight such steps which are used by many author in pre-processing, segmentation and classification methods of lung cancer area detection. If pre-processing and segmentation process have some ambiguity than ultimately it effects on classification process. We discuss such factors briefly so that new researchers can easily understand the situation to work further in which direction.
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Parveen, S. Shaik, and Dr C. Kavitha. "A REVIEW ON COMPUTER AIDED DETECTION AND DIAGNOSIS OF LUNG CANCER NODULES." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 3, no. 3 (November 30, 2012): 393–400. http://dx.doi.org/10.24297/ijct.v3i3a.2944.

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In this paper, a attempt has been made to summarize some of the information about CAD and CADx for the purpose of early detection and diagnosis of lung cancer. Computer Aided Detection (CADe) and Computer Aided Diagnosis (CADx), are procedures in medical information that assist doctors in the interpretation of medical images. Imaging techniques in X-ray, MRI, and Ultrasound diagnostics yield a great deal of information, which the radiologist has to analyze and evaluate comprehensively in a short time. Thus, the use of digital computers to assist practitioners and physicians in diagnosing diseases and to offer a rapid access to medical information gained importance. CAD systems help scan digital images, e.g. from Computed Tomography (CT), for typical appearances and to highlight conspicuous sections, such as focal areas of lung nodules.
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El-Regaily, Salsabil A., Mohammed A. Salem, Mohammed H. Abdel Aziz, and Mohammed I. Roushdy. "Survey of Computer Aided Detection Systems for Lung Cancer in Computed Tomography." Current Medical Imaging Reviews 14, no. 1 (December 28, 2017): 3–18. http://dx.doi.org/10.2174/1573405613666170602123329.

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Bajwa, Usama Ijaz, Abdullah Ali Shah, Muhammad Waqas Anwar, Ghulam Gilanie, and Asma Ejaz Bajwa. "Computer-Aided Detection (CADe) System for Detection of Malignant Lung Nodules in CT Slices - a Key for Early Lung Cancer Detection." Current Medical Imaging Reviews 14, no. 3 (May 8, 2018): 422–29. http://dx.doi.org/10.2174/1573405613666170614083951.

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Moiseenko, B. M., A. A. Meldo, L. V. Utkin, I. Yu Prokhorov, M. A. Ryabinin, and A. A. Bogdanov. "AUTOMATED DETECTION SYSTEM FOR LUNG MASSES AS A STAGE OF ARTIFICIAL INTELLIGENCE DEVELOPMENT IN THE DIAGNOSTICS OF LUNG CANCER." Diagnostic radiology and radiotherapy, no. 3 (November 21, 2018): 62–68. http://dx.doi.org/10.22328/2079-5343-2018-9-3-62-68.

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In the century of the fourth industrial revolution, there is a rapid progress of technological developments in medicine. Possibilities of collecting large amounts of digital information and the modern computer capacity growth are reasons for the increased attention to artificial intelligence (AI) and its role in the diagnostics and the prediction of diseases. In the diagnostics, AI aims to model the human intellectual activity, providing assistance to a practicing doctor in the processing of big data. Development of AI can be considered as a way for implementation and ensuring of national political and economic interests in the health care improvement. Lung cancer is on the first position of cancer incidences. This implies that the development and implementation of computed-aided systems for lung cancer diagnostic is very urgent and important. The article presents the results concerning the development of a computed-aided system for the lung nodule detection, which is based on the processing of computed tomography data. Perspectives of the AI application to the lung cancer diagnostics are discussed. There is a few information about a role of Russian developments in this area in foreign and domestic literature.
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Sato, K., N. Kanno, T. Ishii, and Y. Saijo. "Computer-aided Detection of Lung Tumors in Chest X-ray Images Using a Bone Suppression Algorithm and A Deep Learning Framework." Journal of Physics: Conference Series 2071, no. 1 (October 1, 2021): 012002. http://dx.doi.org/10.1088/1742-6596/2071/1/012002.

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Abstract Detecting lung tumors in early stage by reading chest X-ray images is important for radical treatments of the disease. In order to decrease the risk of missed lung tumors, diagnosis support systems that can provide the accurate detection of lung tumors are in high demand, and the use of artificial intelligence with deep learning is one of the promising solutions. In our research, we aim to improve the accuracy of a deep learning-based system for detecting lung tumors by developing a bone suppression algorithm as a preprocessing for the machine-learning model. Our bone suppression algorithm was devised for conventional single-shot chest X-ray images, which do not rely on a specific type of imaging systems. 604 chest X-ray images were processed using the proposed algorithm and evaluated by combining it with a U-net deep learning model. The results showed that the bone suppression algorithm successfully improved the performance of the deep learning model to identify the location of lung tumors (Intersection over Union) from 0.085 (without the bone suppression algorithm) to 0.142, as well as the ability to classify the lung cancer (Area under Curve) that increased from 0.700 to 0.736. The bone suppression algorithm would be useful to improve the accuracy and the reliability of the deep learning-based diagnosis support systems for detecting lung cancer in mass medical examinations.
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Iqbal, Saleem, Khalid Iqbal, Fahim Arif, Arslan Shaukat, and Aasia Khanum. "Potential Lung Nodules Identification for Characterization by Variable Multistep Threshold and Shape Indices from CT Images." Computational and Mathematical Methods in Medicine 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/241647.

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Computed tomography (CT) is an important imaging modality. Physicians, surgeons, and oncologists prefer CT scan for diagnosis of lung cancer. However, some nodules are missed in CT scan. Computer aided diagnosis methods are useful for radiologists for detection of these nodules and early diagnosis of lung cancer. Early detection of malignant nodule is helpful for treatment. Computer aided diagnosis of lung cancer involves lung segmentation, potential nodules identification, features extraction from the potential nodules, and classification of the nodules. In this paper, we are presenting an automatic method for detection and segmentation of lung nodules from CT scan for subsequent features extraction and classification. Contribution of the work is the detection and segmentation of small sized nodules, low and high contrast nodules, nodules attached with vasculature, nodules attached to pleura membrane, and nodules in close vicinity of the diaphragm and lung wall in one-go. The particular techniques of the method are multistep threshold for the nodule detection and shape index threshold for false positive reduction. We used 60 CT scans of “Lung Image Database Consortium-Image Database Resource Initiative” taken by GE medical systems LightSpeed16 scanner as dataset and correctly detected 92% nodules. The results are reproducible.
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Rosado de Christenson, M. L. "Use of a computer-aided detection system to detect missed lung cancer at chest radiography." Yearbook of Diagnostic Radiology 2010 (January 2010): 3–5. http://dx.doi.org/10.1016/s0098-1672(10)79194-3.

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23

Firmino, Macedo, Antônio H. Morais, Roberto M. Mendoça, Marcel R. Dantas, Helio R. Hekis, and Ricardo Valentim. "Computer-aided detection system for lung cancer in computed tomography scans: Review and future prospects." BioMedical Engineering OnLine 13, no. 1 (2014): 41. http://dx.doi.org/10.1186/1475-925x-13-41.

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White, Charles S., Thomas Flukinger, Jean Jeudy, and Joseph J. Chen. "Use of a Computer-aided Detection System to Detect Missed Lung Cancer at Chest Radiography." Radiology 252, no. 1 (July 2009): 273–81. http://dx.doi.org/10.1148/radiol.2522081319.

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Utreja, Bhawna, Reecha Sharma, and Amit Wason. "A Survey on Segmentation Techniques for Breast Cancer Detection." ECS Transactions 107, no. 1 (April 24, 2022): 6703–9. http://dx.doi.org/10.1149/10701.6703ecst.

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Breast cancer is second most rank disease after lung cancer between ladies around the world. Early detection declines the cancer death rate among women. Computer aided detection (CAD) system have been emerged which help the radiologists by specifying tumor region and reducing error mistake. Segmentation plays an important role in finding tumor area, i.e. Region of Interest (ROI). This paper investigates various segmentation techniques for breast cancer detection. Also, two segmentation techniques, Fuzzy C-Mean (FCM) and K-means, have been applied on mammogram images taken from MIAS database. Results shows that K-means is capable of estimating tumor region boundary as compared to FCM.
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Dr. Samuel Manoharan and Sathish. "Early diagnosis of Lung Cancer with Probability of Malignancy Calculation and Automatic Segmentation of Lung CT scan Images." December 2020 2, no. 4 (December 2020): 175–86. http://dx.doi.org/10.36548/jiip.2020.4.002.

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Computer aided detection system was developed to identify the pulmonary nodules to diagnose the cancer cells. Main aim of this research enables an automated image analysis and malignancy calculation through data and CPU infrastructure. Our proposed algorithm has improvement filter to enhance the imported images and for nodule selection and neural classifier for false reduction. The proposed model is experimented in both internal and external nodules and the obtained results are shown as response characteristics curves.
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Riquelme, Diego, and Moulay Akhloufi. "Deep Learning for Lung Cancer Nodules Detection and Classification in CT Scans." AI 1, no. 1 (January 8, 2020): 28–67. http://dx.doi.org/10.3390/ai1010003.

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Detecting malignant lung nodules from computed tomography (CT) scans is a hard and time-consuming task for radiologists. To alleviate this burden, computer-aided diagnosis (CAD) systems have been proposed. In recent years, deep learning approaches have shown impressive results outperforming classical methods in various fields. Nowadays, researchers are trying different deep learning techniques to increase the performance of CAD systems in lung cancer screening with computed tomography. In this work, we review recent state-of-the-art deep learning algorithms and architectures proposed as CAD systems for lung cancer detection. They are divided into two categories—(1) Nodule detection systems, which from the original CT scan detect candidate nodules; and (2) False positive reduction systems, which from a set of given candidate nodules classify them into benign or malignant tumors. The main characteristics of the different techniques are presented, and their performance is analyzed. The CT lung datasets available for research are also introduced. Comparison between the different techniques is presented and discussed.
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Vijh, Surbhi, Rituparna Sarma, and Sumit Kumar. "Lung Tumor Segmentation Using Marker-Controlled Watershed and Support Vector Machine." International Journal of E-Health and Medical Communications 12, no. 2 (July 2021): 51–64. http://dx.doi.org/10.4018/ijehmc.2021030103.

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The medical imaging technique showed remarkable improvement in interventional treatment of computer-aided medical diagnosis system. Image processing techniques are broadly applied in detection and exploring the abnormalities issues in tumor detection. The early stage of lung tumor detection is extremely important in medical research field. The proposed work uses image processing segmentation technique for detection of lung tumor and the support vector classifier learning technique for predicting stage of tumor. After performing preprocessing and segmentation the features are extracted from region of lung nodule. The classification is performed on dataset acquired from national cancer institute for the evaluation of lung cancer diagnosis. The multi-class machine learning classification technique SVM (support vector machine) identifies the tumor stage of lung dataset. The proposed methodology provides classification of tumor stages and improves the decision-making process. The performance is evaluated by measuring the parameters namely accuracy, sensitivity, and specificity.
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Suárez-Cuenca, Jorge Juan, Amara Tilve, Gonzalo Ferro, Ricardo López, Javier Quiles, and Miguel Souto. "A CAD SCHEME FOR EARLY LUNG CANCER DETECTION IN CHEST RADIOGRAPHY." Biomedical Engineering: Applications, Basis and Communications 29, no. 05 (October 2017): 1750037. http://dx.doi.org/10.4015/s1016237217500375.

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The purpose of this work is to describe a chest radiography computer-aided diagnostic (CAD) scheme designed to analyze the chest radiographs performed in the framework of the Galician (Spain) Health Service (GHS), including the radiographs that are not reported by the radiologists. The final goal of this CAD system is its integration in the GHS daily clinical environment, with a feasible RIS-PACS-CAD and EHR-CAD integration model. The database of the study included 55 chest radiographies with 64 nodules/lung cancer. This database was used to develop and test the CAD system in our research laboratory. Free-Response Receiver Operating Characteristic (FROC) curves were employed to evaluate the performance of the CAD system. An independent database was employed to evaluate the performance of the CAD system by external radiologists. After the application of a linear classifier, our CAD system achieved a sensitivity of 70% with a false positive rate between 4 and 6 per image depending on the testing database. When compared with other commercial systems, our CAD scheme achieved similar performance results. Therefore, our CAD scheme could be utilized to help radiologists in the detection of lung nodules in chest radiography, and therefore, it can be integrated in the clinical practice.
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Wang, Xun, Lisheng Wang, and Pan Zheng. "SC-Dynamic R-CNN: A Self-Calibrated Dynamic R-CNN Model for Lung Cancer Lesion Detection." Computational and Mathematical Methods in Medicine 2022 (March 28, 2022): 1–9. http://dx.doi.org/10.1155/2022/9452157.

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Lung cancer has complex biological characteristics and a high degree of malignancy. It has always been the number one “killer” in cancer, threatening human life and health. The diagnosis and early treatment of lung cancer still require improvement and further development. With high morbidity and mortality, there is an urgent need for an accurate diagnosis method. However, the existing computer-aided detection system has a complicated process and low detection accuracy. To solve this problem, this paper proposed a two-stage detection method based on the dynamic region-based convolutional neural network (Dynamic R-CNN). We divide lung cancer into squamous cell carcinoma, adenocarcinoma, and small cell carcinoma. By adding the self-calibrated convolution module into the feature network, we extracted more abundant lung cancer features and proposed a new regression loss function to further improve the detection performance of lung cancer. After experimental verification, the mAP (mean average precision) of the model can reach 88.1% on the lung cancer dataset and it performed particularly well with a high IoU (intersection over union) threshold. This method has a good performance in the detection of lung cancer and can improve the efficiency of doctors’ diagnoses. It can avoid false detection and miss detection to a certain extent.
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31

Satra, Henil. "Lung Nodule Detection using Segmentation Approach for Computed Tomography Scan Images." International Journal for Research in Applied Science and Engineering Technology 9, no. 9 (September 30, 2021): 1778–90. http://dx.doi.org/10.22214/ijraset.2021.38258.

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Abstract: Lung disorders have become really common in today’s world due to growing amount of air pollution, our increased exposure to harmful radiations and our unhealthy lifestyles. Hence, the diagnosis of lung disorders has become of paramount importance. The commonly used Thresholding approaches and morphological operations often fail to detect the peripheral pathology bearing areas. Hence, we present the segmentation approach of the lung tissue for computer aided diagnosis system. We use a novel technique for segmentation of lungs from CT scan (Computed Tomography) of the chest or upper torso. The accuracy of analysis and its implication majorly depends on the kind of segmentation technique used. Hence, it is important that the method used is highly reliable and is successful in nodule detection and classification. We use MATLAB and OpenCV libraries to apply segmentation on CT scan images to get the desired output. We have also created a working proprietary user interface called “PULMONIS” for the ease of doctors and patients to upload the CT scan images and get the output after the image processing is done in the backend. Keywords: Lung nodule detection, Image Processing, Computed Tomography, Image Segmentation, Lung Cancer, Contour Segmentation, MATLAB, OpenCV, Computer Vision.
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32

Kasinathan, Gopi, and Selvakumar Jayakumar. "Cloud-Based Lung Tumor Detection and Stage Classification Using Deep Learning Techniques." BioMed Research International 2022 (January 10, 2022): 1–17. http://dx.doi.org/10.1155/2022/4185835.

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Artificial intelligence (AI), Internet of Things (IoT), and the cloud computing have recently become widely used in the healthcare sector, which aid in better decision-making for a radiologist. PET imaging or positron emission tomography is one of the most reliable approaches for a radiologist to diagnosing many cancers, including lung tumor. In this work, we proposed stage classification of lung tumor which is a more challenging task in computer-aided diagnosis. As a result, a modified computer-aided diagnosis is being considered as a way to reduce the heavy workloads and second opinion to radiologists. In this paper, we present a strategy for classifying and validating different stages of lung tumor progression, as well as a deep neural model and data collection using cloud system for categorizing phases of pulmonary illness. The proposed system presents a Cloud-based Lung Tumor Detector and Stage Classifier (Cloud-LTDSC) as a hybrid technique for PET/CT images. The proposed Cloud-LTDSC initially developed the active contour model as lung tumor segmentation, and multilayer convolutional neural network (M-CNN) for classifying different stages of lung cancer has been modelled and validated with standard benchmark images. The performance of the presented technique is evaluated using a benchmark image LIDC-IDRI dataset of 50 low doses and also utilized the lung CT DICOM images. Compared with existing techniques in the literature, our proposed method achieved good result for the performance metrics accuracy, recall, and precision evaluated. Under numerous aspects, our proposed approach produces superior outcomes on all of the applied dataset images. Furthermore, the experimental result achieves an average lung tumor stage classification accuracy of 97%-99.1% and an average of 98.6% which is significantly higher than the other existing techniques.
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Et. al., Er Charnpreet kaur,. "Artificial Intelligence Techniques for Cancer Detection in Medical Image Processing: A Review." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (April 10, 2021): 2667–73. http://dx.doi.org/10.17762/turcomat.v12i2.2286.

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Cancer is the uncontrolled growth of abnormal cells in any part of a body. Cancer is a broad term for a group of diseases caused when abnormal cells grows in different body parts. There are more than hundred types of Cancer such as Lung cancer, Breast cancer, Skin cancer, Oral cancer, Colon cancer and Prostate cancer. Delay in treatment can cause serious health issues, even cause loss of life. This paper gives the review on methods of detection of lung cancer and brain cancer and liver using image processing. The methods used for detection are Automated and computer-aided detection system (CAD) with artificial intelligence and these methods are good to process a large datasets to provide accurate and efficient results in the detection of cancer. However, these processing system have to face many challenges to implement on large scale including imageacquisition, pre-processing, segmentation, and data management and classification strategies to be compatible with AI. This paper reviews the various image acquisition and segmentation techniques. These techniques become the need of an hour to cater the growing patient population and for the improvement in the Healthcare system.
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34

Brown, Matthew S., Richard Pais, Peiyuan Qing, Sumit Shah, Michael F. McNitt-Gray, Jonathan G. Goldin, Iva Petkovska, Lien Tran, and Denise R. Aberle. "An Architecture for Computer-Aided Detection and Radiologic Measurement of Lung Nodules in Clinical Trials." Cancer Informatics 4 (January 2007): 117693510700400. http://dx.doi.org/10.1177/117693510700400001.

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Computer tomography (CT) imaging plays an important role in cancer detection and quantitative assessment in clinical trials. High-resolution imaging studies on large cohorts of patients generate vast data sets, which are infeasible to analyze through manual interpretation. In this article we describe a comprehensive architecture for computer-aided detection (CAD) and surveillance on lung nodules in CT images. Central to this architecture are the analytic components: an automated nodule detection system, nodule tracking capabilities and volume measurement, which are integrated within a data management system that includes mechanisms for receiving and archiving images, a database for storing quantitative nodule measurements and visualization, and reporting tools. We describe two studies to evaluate CAD technology within this architecture, and the potential application in large clinical trials. The first study involves performance assessment of an automated nodule detection system and its ability to increase radiologist sensitivity when used to provide a second opinion. The second study investigates nodule volume measurements on CT made using a semi-automated technique and shows that volumetric analysis yields significantly different tumor response classifications than a 2D diameter approach. These studies demonstrate the potential of automated CAD tools to assist in quantitative image analysis for clinical trials.
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Shaffie, Ahmed, Ahmed Soliman, Amr Eledkawy, Victor van Berkel, and Ayman El-Baz. "Computer-Assisted Image Processing System for Early Assessment of Lung Nodule Malignancy." Cancers 14, no. 5 (February 22, 2022): 1117. http://dx.doi.org/10.3390/cancers14051117.

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Lung cancer is one of the most dreadful cancers, and its detection in the early stage is very important and challenging. This manuscript proposes a new computer-aided diagnosis system for lung cancer diagnosis from chest computed tomography scans. The proposed system extracts two different kinds of features, namely, appearance features and shape features. For the appearance features, a Histogram of oriented gradients, a Multi-view analytical Local Binary Pattern, and a Markov Gibbs Random Field are developed to give a good description of the lung nodule texture, which is one of the main distinguishing characteristics between benign and malignant nodules. For the shape features, Multi-view Peripheral Sum Curvature Scale Space, Spherical Harmonics Expansion, and a group of some fundamental morphological features are implemented to describe the outer contour complexity of the nodules, which is main factor in lung nodule diagnosis. Each feature is fed into a stacked auto-encoder followed by a soft-max classifier to generate the initial malignancy probability. Finally, all these probabilities are combined together and fed to the last network to give the final diagnosis. The system is validated using 727 nodules which are subset from the Lung Image Database Consortium (LIDC) dataset. The system shows very high performance measures and achieves 92.55%, 91.70%, and 93.40% for the accuracy, sensitivity, and specificity, respectively. This high performance shows the ability of the system to distinguish between the malignant and benign nodules precisely.
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Rejiram R and Kanniga E. "Content Based Image Retrieval System for Lung Cancer Detection Using Neural Network and Circular Hough Transform." International Journal of Research in Pharmaceutical Sciences 11, no. 4 (December 25, 2020): 7518–24. http://dx.doi.org/10.26452/ijrps.v11i4.3957.

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Computer Aided Detection (CAD) systems that automatic detection and localize lung nodules in CT scans. A major problem in this system is a large number of false positives because of no provision for comparison of the predicted output. This paper recommends a new system with a combination of CBIR and neural network to full fill the gap in the area of early detection of lung cancer. From the preprocessed CT scan image, the system identifies whether it contains nodules using Circular Hough Transform and classifies into benign or malignant nodule using Probabilistic Neural Network. Then, it searched for the most identical pictures and retrieved it from the database. From the retrieved image, it is easy to identify the present cancer stage of the patient. Experiments have done based on both LIDC database and the locally collected database. The performance evaluation of the system is done by using both. The experimental results show that the present study easily differentiates benign and malignant nodules with an efficiency of 97 % accuracy on LIDC dataset, 95 % accuracy on Local dataset and similar images are retrieved with its present stage from the available database with a higher precision and recall rate.
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37

Rejiram R and Kanniga E. "Content Based Image Retrieval System for Lung Cancer Detection Using Neural Network and Circular Hough Transform." International Journal of Research in Pharmaceutical Sciences 11, no. 4 (December 25, 2020): 7518–24. http://dx.doi.org/10.26452/ijrps.v11i4.3957.

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Computer Aided Detection (CAD) systems that automatic detection and localize lung nodules in CT scans. A major problem in this system is a large number of false positives because of no provision for comparison of the predicted output. This paper recommends a new system with a combination of CBIR and neural network to full fill the gap in the area of early detection of lung cancer. From the preprocessed CT scan image, the system identifies whether it contains nodules using Circular Hough Transform and classifies into benign or malignant nodule using Probabilistic Neural Network. Then, it searched for the most identical pictures and retrieved it from the database. From the retrieved image, it is easy to identify the present cancer stage of the patient. Experiments have done based on both LIDC database and the locally collected database. The performance evaluation of the system is done by using both. The experimental results show that the present study easily differentiates benign and malignant nodules with an efficiency of 97 % accuracy on LIDC dataset, 95 % accuracy on Local dataset and similar images are retrieved with its present stage from the available database with a higher precision and recall rate.
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38

Khalid, Shehzad, Anwar C. Shaukat, Amina Jameel, and Imran Fareed. "Segmentation of Lung Nodules in CT Scan Data." International Journal of Privacy and Health Information Management 3, no. 2 (July 2015): 66–77. http://dx.doi.org/10.4018/ijphim.2015070104.

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Developing an effective computer-aided diagnosis (CAD) system for lung cancer is of great clinical importance and can increase the patient's chance of survival. For this reason, CAD systems for lung cancer have been investigated in a huge number of research studies. Several studies have shown the feasibility and robustness of automated matching of corresponding nodule pairs between follow up examinations. Different image pre-processing and segmentation techniques are used in various research sides to segment different tumors or ulcers from different images. This paper aims to make a review on the existing segmentation algorithms used for CT images of pulmonary nodules and presents a study of the existing methods on automated lung nodule detection. It provides a comparison of the performance of the existing approaches in regards to effective domain results.
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39

Cui, X., S. Zheng, M. Heuvelmans, Y. Du, G. Sidorenkov, M. Dorrius, R. Veldhuis, et al. "P42.02 Evaluating the Feasibility of a Deep Learning-Based Computer-Aided Detection System for Lung Nodule Detection in a Lung Cancer Screening Program." Journal of Thoracic Oncology 16, no. 3 (March 2021): S477—S478. http://dx.doi.org/10.1016/j.jtho.2021.01.826.

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40

Alharbi, Abir. "An Automated Computer System Based on Genetic Algorithm and Fuzzy Systems for Lung Cancer Diagnosis." International Journal of Nonlinear Sciences and Numerical Simulation 19, no. 6 (September 25, 2018): 583–94. http://dx.doi.org/10.1515/ijnsns-2017-0048.

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AbstractAn automated system for the diagnosis of lung cancer is proposed in this paper, the system is designed by combining two major methodologies, namely the fuzzy base systems and the evolutionary genetic algorithms (GAs), to be employed on lung cancer data to assist physicians in the early detection of lung cancers, and hence obtain an early automated diagnosis complementary to that by physicians. Our hybrid algorithm, the genetic-fuzzy algorithm, has produced optimized diagnosis systems that attain high classification performance, in fact, our best six rule system obtained a 97.5 % accuracy, with simple and well interpretive rules, with 93 % degree of confidence, and without the need for dimensionality reduction. The results on real data indicate that the proposed system is very effective in the diagnosis of lung cancer and can be used for clinical applications.
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41

Xiao, Zhitao, Naichao Du, Lei Geng, Fang Zhang, Jun Wu, and Yanbei Liu. "Multi-Scale Heterogeneous 3D CNN for False-Positive Reduction in Pulmonary Nodule Detection, Based on Chest CT Images." Applied Sciences 9, no. 16 (August 9, 2019): 3261. http://dx.doi.org/10.3390/app9163261.

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Currently, lung cancer has one of the highest mortality rates because it is often caught too late. Therefore, early detection is essential to reduce the risk of death. Pulmonary nodules are considered key indicators of primary lung cancer. Developing an efficient and accurate computer-aided diagnosis system for pulmonary nodule detection is an important goal. Typically, a computer-aided diagnosis system for pulmonary nodule detection consists of two parts: candidate nodule extraction and false-positive reduction of candidate nodules. The reduction of false positives (FPs) of candidate nodules remains an important challenge due to morphological characteristics of nodule height changes and similar characteristics to other organs. In this study, we propose a novel multi-scale heterogeneous three-dimensional (3D) convolutional neural network (MSH-CNN) based on chest computed tomography (CT) images. There are three main strategies of the design: (1) using multi-scale 3D nodule blocks with different levels of contextual information as inputs; (2) using two different branches of 3D CNN to extract the expression features; (3) using a set of weights which are determined by back propagation to fuse the expression features produced by step 2. In order to test the performance of the algorithm, we trained and tested on the Lung Nodule Analysis 2016 (LUNA16) dataset, achieving an average competitive performance metric (CPM) score of 0.874 and a sensitivity of 91.7% at two FPs/scan. Moreover, our framework is universal and can be easily extended to other candidate false-positive reduction tasks in 3D object detection, as well as 3D object classification.
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42

Brown, Matthew S., Hyun J. Kim, Pechin Lo, Eran Barnoy, Michael F. McNitt-Gray, Denise R. Aberle, and Jonathan Goldin. "Automated tumor size assessment: Consistency of computer measurements with an expert panel." Journal of Clinical Oncology 31, no. 15_suppl (May 20, 2013): 7566. http://dx.doi.org/10.1200/jco.2013.31.15_suppl.7566.

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7566 Background: Manual tumor measurements for use in diagnosis and longitudinal assessment suffer from intra- and inter-observer variability. In practice they are also limited to 1-dimensional diameter measurements because manual contouring of 3D tumor boundaries is impractical. In this work we evaluated a fully automated tumor assessment system in the setting of lung nodules on CT by comparing its tumor size and density measurements against independent measurements made by an expert panel of radiologists. Methods: A new computer-aided detection (CAD) system has been developed that performs fully automated lung nodule detection and measurement. In order to identify the nodule boundary in 3D the system performs automated intensity thresholding, a Euclidean Distance Transformation, and segmentation based on watersheds. The system computes nodule diameter, volume, and mean density in Hounsfield Units (HU). The automated measurements were evaluated against data from the publically available Lung Imaging Database Consortium (LIDC), where each CT scan was reviewed and annotated (with volumetric tumor contours) by an expert panel of four radiologists. Nodule were included from consecutive subjects in the database that were ≥ 4 mm. The CT slice thickness ranged from 0.6 – 3.0 mm. For each nodule measure, the intra-class correlation coefficient (ICC) was computed among the four radiologists and then re-computed with CAD as a fifth observer. Results: 51 lung nodules from 44 subjects were analyzed. The ICCs were computed as shown in the table using a log transformation for volume and diameter. All nodule measures have similar ICCs and overlapping confidence intervals (CI). Conclusions: A new fully automated CAD system has been developed that provides CT lung nodule measurements that are consistent with an expert panel of radiologists. The automated computer system enables practical 3D tumor assessment and reduces measurement variability, which has implications for longitudinal studies. [Table: see text]
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43

Murugesan, Malathi, Kalaiselvi Kaliannan, Shankarlal Balraj, Kokila Singaram, Thenmalar Kaliannan, and Johny Renoald Albert. "A Hybrid deep learning model for effective segmentation and classification of lung nodules from CT images." Journal of Intelligent & Fuzzy Systems 42, no. 3 (February 2, 2022): 2667–79. http://dx.doi.org/10.3233/jifs-212189.

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Deep learning algorithms will be used to detect lung nodule anomalies at an earlier stage. The primary goal of this effort is to properly identify lung cancer, which is critical in preserving a person’s life. Lung cancer has been a source of concern for people all around the world for decades. Several researchers presented numerous issues and solutions for various stages of a computer-aided system for diagnosing lung cancer in its early stages, as well as information about lung cancer. Computer vision is one of the field of artificial intelligence this is a better way to detect and prevent the lung cancer. This study focuses on the stages involved in detecting lung tumor regions, namely pre-processing, segmentation, and classification models. An adaptive median filter is used in pre-processing to identify the noise. The work’s originality seeks to create a simple yet effective model for the rapid identification and U-net architecture based segmentation of lung nodules. This approach focuses on the identification and segmentation of lung cancer by detecting picture normalcy and abnormalities.
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44

Li, Rui, Chuda Xiao, Yongzhi Huang, Haseeb Hassan, and Bingding Huang. "Deep Learning Applications in Computed Tomography Images for Pulmonary Nodule Detection and Diagnosis: A Review." Diagnostics 12, no. 2 (January 25, 2022): 298. http://dx.doi.org/10.3390/diagnostics12020298.

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Lung cancer has one of the highest mortality rates of all cancers and poses a severe threat to people’s health. Therefore, diagnosing lung nodules at an early stage is crucial to improving patient survival rates. Numerous computer-aided diagnosis (CAD) systems have been developed to detect and classify such nodules in their early stages. Currently, CAD systems for pulmonary nodules comprise data acquisition, pre-processing, lung segmentation, nodule detection, false-positive reduction, segmentation, and classification. A number of review articles have considered various components of such systems, but this review focuses on segmentation and classification parts. Specifically, categorizing segmentation parts based on lung nodule type and network architectures, i.e., general neural network and multiview convolution neural network (CNN) architecture. Moreover, this work organizes related literature for classification of parts based on nodule or non-nodule and benign or malignant. The essential CT lung datasets and evaluation metrics used in the detection and diagnosis of lung nodules have been systematically summarized as well. Thus, this review provides a baseline understanding of the topic for interested readers.
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45

Traverso, A., E. Lopez Torres, M. E. Fantacci, and P. Cerello. "Computer-aided detection systems to improve lung cancer early diagnosis: state-of-the-art and challenges." Journal of Physics: Conference Series 841 (May 2017): 012013. http://dx.doi.org/10.1088/1742-6596/841/1/012013.

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46

Bhatt, Shital D., and Himanshu B. Soni. "Improving Classification Accuracy of Pulmonary Nodules using Simplified Deep Neural Network." Open Biomedical Engineering Journal 15, no. 1 (December 31, 2021): 180–89. http://dx.doi.org/10.2174/1874120702115010180.

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Background: Lung cancer is among the major causes of death in the world. Early detection of lung cancer is a major challenge. These encouraged the development of Computer-Aided Detection (CAD) system. Objectives: We designed a CAD system for performance improvement in detecting and classifying pulmonary nodules. Though the system will not replace radiologists, it will be helpful to them in order to accurately diagnose lung cancer. Methods: The architecture comprises of two steps, among which in the first step CT scans are pre-processed and the candidates are extracted using the positive and negative annotations provided along with the LUNA16 dataset, and the second step consists of three different neural networks for classifying the pulmonary nodules obtained from the first step. The models in the second step consist of 2D-Convolutional Neural Network (2D-CNN), Visual Geometry Group-16 (VGG-16) and simplified VGG-16, which independently classify pulmonary nodules. Results: The classification accuracies achieved for 2D-CNN, VGG-16 and simplified VGG-16 were 99.12%, 98.17% and 99.60%, respectively. Conclusion: The integration of deep learning techniques along with machine learning and image processing can serve as a good means of extracting pulmonary nodules and classifying them with improved accuracy. Based on these results, it can be concluded that the transfer learning concept will improve system performance. In addition, performance improves proper designing of the CAD system by considering the amount of dataset and the availability of computing power.
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47

Mousa, Doaa, Nourhan Zayed, and Mahmoud Fakhr. "SIGNIFICANT FEATURES TO DETECT PULMONARY NODULES FROM CT LUNG IMAGES." Biomedical Engineering: Applications, Basis and Communications 29, no. 06 (December 2017): 1750045. http://dx.doi.org/10.4015/s1016237217500454.

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Lung cancer causes the most number of deaths worldwide in both men and women. Early detection and diagnosis can minimize the disease mortality rate. Commonly, chest computed tomography (CT) scans are used by clinicians to diagnose lung cancer. The lung cancer diagnosis relies on detection of the pulmonary nodules in CT scans. In this paper, we propose computer-aided diagnostic systems that can define and suggest the most important features that can distinguish lung nodule from nonnodule one. The proposed system can be described through the following six steps: (a) Patch Extraction, (b) Image Preprocessing, (c) Feature Extraction, (d) Normalization, (e) Feature Reduction, and (f) Patch Classification. Feature extraction and selection are the most important steps in any disease classification process. A combination of 132 texture features with three shape-based features has been extracted. Then the normalization step has been done using min–max method followed by the feature reduction step based on the wrapper approach. The feature reduction step resulted in selecting a set of eight features for the classification process. The algorithm was developed and tested using 166 patches of CT images. The selected eight features achieve accuracy 96.5% using [Formula: see text]-nearest neighbor classifier. The results were validated using the cross-validation technique, [Formula: see text]-fold method.
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48

Mekali, Vijayalaxmi, and Girijamma H. A. "Fully Automatic Detection and Segmentation Approach for Juxta-Pleural Nodules From CT Images." International Journal of Healthcare Information Systems and Informatics 16, no. 2 (April 2021): 87–104. http://dx.doi.org/10.4018/ijhisi.20210401.oa5.

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Early detection of all types of lung nodules with different characters in medical modality images using computer-aided detection is the best acceptable remedy to save the lives of lung cancer sufferers. But accuracy of different types of nodule detection rates is based on chosen segmented procedures for parenchyma and nodules. Separation of pleural from juxta-pleural nodules (JPNs) is difficult as intensity of pleural and attached nodule is similar. This research paper proposes a fully automated method to detect and segment JPNs. In the proposed method, lung parenchyma is segmented using iterative thresholding algorithm. To improve the nodules detection rate separation of connected lung lobes, an algorithm is proposed to separate connected left and right lung lobes. The new method segments JPNs based on lung boundary pixels extraction, concave points extraction, and separation of attached pleural from nodule. Validation of the proposed method was performed on LIDC-CT images. The experimental result confirms that the developed method segments the JPNs with less computational time and high accuracy.
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Karrar, Ayat, Mai S. Mabrouk, and Manal AbdEl Wahed. "DIAGNOSIS OF LUNG NODULES FROM 2D COMPUTER TOMOGRAPHY SCANS." Biomedical Engineering: Applications, Basis and Communications 32, no. 03 (June 2020): 2050017. http://dx.doi.org/10.4015/s1016237220500179.

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Cancers typically are both highly dangerous and common. Among these, lung cancer has one of the lowest survival rates compared to other cancers. CT scans can reveal dense masses of different shapes and sizes; in the lungs, these are called lung nodules. This study applied a computer-aided diagnosis (CAD) system to detect candidate nodules — and diagnose it either solitary or juxtapleural — with equivalent diameters, ranging from 7.78[Formula: see text]mm to 22.48[Formula: see text]mm in a 2D CT slice. Pre-processing and segmentation is a very important step to segment and enhance the CT image. A segmentation and enhancement algorithm is achieved using bilateral filtering, Thresholding the gray-level transformation function, Bounding box and maximum intensity projection. Border artifacts are removed by clearing the lung border, erosion, dilation and superimposing. Feature extraction is done by extracting 20 gray-level co-occurrence matrix features from four directions: [Formula: see text], [Formula: see text], [Formula: see text] and [Formula: see text] and one distance of separation ([Formula: see text] pixel). In the classification step, two classifiers are proposed to classify two types of nodules based on their locations: as juxtapleural or solitary nodules. The two classifiers are a deep learning convolutional neural network (CNN) and the K-nearest neighbor (KNN) algorithm. Random oversampling and 10-fold cross-validation are used to improve the results. In our CAD system, the highest accuracy and sensitivity rates achieved by the CNN were 96% and 95%, respectively, for solitary nodule detection. The highest accuracy and sensitivity rates achieved by the KNN model were 93.8% and 96.7%, respectively, and K was set to 1 to detect juxtapleural nodules.
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50

Amer, Hanan M., Fatma E. Abou-Chadi, Sherif S. Kishk, and Marwa I. Obayya. "A CAD System for the Early Detection of Lung Nodules Using Computed Tomography Scan Images." International Journal of Online and Biomedical Engineering (iJOE) 15, no. 04 (February 27, 2019): 40. http://dx.doi.org/10.3991/ijoe.v15i04.9837.

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<p>In this paper, a computer-aided detection system is developed to detect lung nodules at an early stage using Computed Tomography (CT) scan images where lung nodules are one of the most important indicators to predict lung cancer. The developed system consists of four stages. First, the raw Computed Tomography lung images were preprocessed to enhance the image contrast and eliminate noise. Second, an automatic segmentation procedure for human's lung and pulmonary nodule canddates (nodules, blood vessels) using a two-level thresholding technique and morphological operations. Third, a feature fusion technique that fuses four feature extraction techniques: the statistical features of first and second order, value histogram features, histogram of oriented gradients features, and texture features of gray level co-occurrence matrix based on wavelet coefficients was utilised to extract the main features. The fourth stage is the classifier. Three classifiers were used and their performance was compared in order to obtain the highest classification accuracy. These are; multi-layer feed-forward neural network, radial basis function neural network and support vector machine. The performance of the proposed system was assessed using three quantitative parameters. These are: the classification accuracy rate, the sensitivity and the specificity. Forty standard computed tomography images containing 320 regions of interest obtained from an early lung cancer action project association were used to test and evaluate the developed system. The images consists of 40 computed tomography scan images. The results have shown that the fused features vector resulting from genetic algorithm as a feature selection technique and the support vector machine classifier give the highest classification accuracy rate, sensitivity and specificity values of 99.6%, 100% and 99.2%, respectively.</p>
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