Relevant bibliographies by topics / Chest X-ray image

Academic literature on the topic 'Chest X-ray image'

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Published: 30 May 2022
Last updated: 31 May 2022

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Journal articles on the topic "Chest X-ray image"

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Altun, Armagan, and Okan Erdogan. "A Chest X-Ray Image." Cardiology in Review 11, no. 6 (November 2003): 301–2. http://dx.doi.org/10.1097/01.crd.0000089526.05199.04.

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Wu, Huaiguang, Pengjie Xie, Huiyi Zhang, Daiyi Li, and Ming Cheng. "Predict pneumonia with chest X-ray images based on convolutional deep neural learning networks." Journal of Intelligent & Fuzzy Systems 39, no. 3 (October 7, 2020): 2893–907. http://dx.doi.org/10.3233/jifs-191438.

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The chest X-ray examination is one of the most important methods for screening and diagnosing of many lung diseases. Diagnosis of pneumonia by chest X-ray is one of the common methods used by medical experts. However, the image quality of chest X-Ray has some defects, such as low contrast, overlapping organs and blurred boundary, which seriously affects detecting pneumonia in chest X-rays. Therefore, it has important medical value and application significance to construct a stable and accurate automatic detection model of pneumonia through a large number of chest X-ray images. In this paper, we propose a novel hybrid system for detecting pneumonia from chest X-Ray image: ACNN-RF, which is an adaptive median filter Convolutional Neural Network (CNN) recognition model based on Random forest (RF). Firstly, the improved adaptive median filtering is employed to remove noise in the chest X-ray image, which makes the image more easily recognized. Secondly, we establish the CNN architecture based on Dropout to extract deep activation features from each chest X-ray image. Finally, we employ the RF classifier based on GridSearchCV class as a classifier for deep activation features in CNN model. It not only avoids the phenomenon of over-fitting in data training, but also improves the accuracy of image classification. During our experiment, the public chest X-ray image dataset used in the experiment contains 5863 images, which comprises 4265 frontal-view X-ray images of 1574 unique patients. The average recognition rate of pneumonia is up to 97% by the proposed ACNN-RF. The experimental results show that the ACNN-RF identification system is more effective than the previous traditional image identification system.
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Park, So Yeon, and Byung Cheol Song. "Image Quality Enhancement for Chest X-ray images." Journal of the Institute of Electronics and Information Engineers 52, no. 10 (October 25, 2015): 97–107. http://dx.doi.org/10.5573/ieie.2015.52.10.097.

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Ismail, Azlan, Taufik Rahmat, and Sharifah Aliman. "CHEST X-RAY IMAGE CLASSIFICATION USING FASTER R-CNN." MALAYSIAN JOURNAL OF COMPUTING 4, no. 1 (July 1, 2019): 225. http://dx.doi.org/10.24191/mjoc.v4i1.6095.

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Chest x-ray image analysis is the common medical imaging exam needed to assess different pathologies. Having an automated solution for the analysis can contribute to minimizing the workloads, improve efficiency and reduce the potential of reading errors. Many methods have been proposed to address chest x-ray image classification and detection. However, the application of regional-based convolutional neural networks (CNN) is currently limited. Thus, we propose an approach to classify chest x-ray images into either one of two categories, pathological or normal based on Faster Regional-CNN model. This model utilizes Region Proposal Network (RPN) to generate region proposals and perform image classification. By applying this model, we can potentially achieve two key goals, high confidence in the classification and reducing the computation time. The results show the applied model achieved higher accuracy as compared to the medical representatives on the random chest x-ray images. The classification model is also reasonably effective in classifying between finding and normal chest x-ray image captured through a live webcam.
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Widodo, Chomsin S., Agus Naba, Muhammad M. Mahasin, Yuyun Yueniwati, Terawan A. Putranto, and Pangeran I. Patra. "UBNet: Deep learning-based approach for automatic X-ray image detection of pneumonia and COVID-19 patients." Journal of X-Ray Science and Technology 30, no. 1 (January 22, 2022): 57–71. http://dx.doi.org/10.3233/xst-211005.

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BACKGROUND: Analysis of chest X-ray images is one of the primary standards in diagnosing patients with COVID-19 and pneumonia, which is faster than using PCR Swab method. However, accuracy of using X-ray images needs to be improved. OBJECTIVE: To develop a new deep learning system of chest X-ray images and evaluate whether it can quickly and accurately detect pneumonia and COVID-19 patients. METHODS: The developed deep learning system (UBNet v3) uses three architectural hierarchies, namely first, to build an architecture containing 7 convolution layers and 3 ANN layers (UBNet v1) to classify between normal images and pneumonia images. Second, using 4 layers of convolution and 3 layers of ANN (UBNet v2) to classify between bacterial and viral pneumonia images. Third, using UBNet v1 to classify between pneumonia virus images and COVID-19 virus infected images. An open-source database with 9,250 chest X-ray images including 3,592 COVID-19 images were used in this study to train and test the developed deep learning models. RESULTS: CNN architecture with a hierarchical scheme developed in UBNet v3 using a simple architecture yielded following performance indices to detect chest X-ray images of COVID-19 patients namely, 99.6%accuracy, 99.7%precision, 99.7%sensitivity, 99.1%specificity, and F1 score of 99.74%. A desktop GUI-based monitoring and classification system supported by a simple CNN architecture can process each chest X-ray image to detect and classify COVID-19 image with an average time of 1.21 seconds. CONCLUSION: Using three hierarchical architectures in UBNet v3 improves system performance in classifying chest X-ray images of pneumonia and COVID-19 patients. A simple architecture also speeds up image processing time.
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Kalidasan, S. "COVID-19 Detection with X-Ray and CT-Scan Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 1 (January 31, 2022): 1526–29. http://dx.doi.org/10.22214/ijraset.2022.40096.

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Abstract: We researched the diagnostic capabilities of deep learning on chest radiographs and an image classifier based on the COVID-Net was presented to classify chest MRIimages. In the case of a small amount of COVID-19 data, data enhancement was proposed to expanded COVID-19 data 17 times. Our model aims at transfer learning, model integration and classify chest MRI images according to three labels: normal, COVID-19 and viral pneumonia. According to the accuracy and loss value, choose the models ResNet-101 and ResNet-152 with good effect for fusion, and dynamically improve their weight ratio during the training process. After training, the model can achieve 96.1% of the types of chest MRI images accuracy on the test set. This technology has higher sensitivity than radiologists in the screening and diagnosis of lung nodules. As an auxiliary diagnostic technology, it can help radiologists improve work efficiency and diagnostic accuracy. COVID-19 is posed as very infectious and deadly pneumonia type disease until recent time. Despite having lengthy testing time, RT-PCR is a proven testing methodology to detect corona virus infection. Sometimes, it might give more false positive and false negative results than the desired rates. Therefore, to assist the traditional RT-PCR methodology for accurate clinical diagnosis, COVID-19 screening can be adopted with X-Ray and CT scan images of lung of an individual. This image based diagnosis will bring radical change in detecting corona virus infection in human body with ease and having zero or near to zero false positives and false negatives rates. This paper reports a convolution neural network (CNN) based multi-image augmentation technique for detecting COVID-19 in chest X-Ray and chest CT scan images of corona virus suspected individuals. Multi-image augmentation makes use of discontinuity information obtained in the filtered images for increasing the number of effective examples for training the CNN model. With this approach, the proposed model exhibits higher classification accuracy around 95.38% and 98.97% for CT scan and X-Ray images respectively. CT scan images with multi-image augmentation chieves sensitivity of 94.78% and specificity of 95.98%, whereas X-Ray images with multi-image augmentation achieves sensitivity of 99.07% and specificity of 98.88%. Evaluation has been done on publicly available databases containing both chest X-Ray and CT scan images and the experimental results are also compared with ResNet-50 and VGG-16 models. Keywords: Machine Learning, convolution neural network, MRI images.
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Rumyantsev, Aleksei Aleksandrovich, Farkhad Mansurovich Bikmuratov, and Nikolai Pavlovich Pashin. "Entropy estimation of the fragments of chest X-ray images." Кибернетика и программирование, no. 1 (January 2021): 20–26. http://dx.doi.org/10.25136/2644-5522.2021.1.31676.

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The subject of this research is medical chest X-ray images. After fundamental pre-processing, the accumulated database of such images can be used for training deep convolutional neural networks that have become one of the most significant innovations in recent years. The trained network carries out preliminary binary classification of the incoming images and serve as an assistant to the radiotherapist. For this purpose, it is necessary to train the neural network to carefully minimize type I and type II errors. Possible approach towards improving the effectiveness of application of neural networks, by the criteria of reducing computational complexity and quality of image classification, is the auxiliary approaches: image pre-processing and preliminary calculation of entropy of the fragments. The article provides the algorithm for X-ray image pre-processing, its fragmentation, and calculation of the entropy of separate fragments. In the course of pre-processing, the region of lungs and spine is selected, which comprises approximately 30-40% of the entire image. Then the image is divided into the matrix of fragments, calculating the entropy of separate fragments in accordance with Shannon’s formula based pm the analysis of individual pixels. Determination of the rate of occurrence of each of the 255 colors allows calculating the total entropy. The use of entropy for detecting pathologies is based on the assumption that its values differ for separate fragments and overall picture of its distribution between the images with the norm and pathologies. The article analyzes the statistical values: standard deviation of error, dispersion. A fully connected neural network is used for determining the patterns in distribution of entropy and its statistical characteristics on various fragments of the chest X-ray image.
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Mogaveera, Rachita, Roshan Maur, Zeba Qureshi, and Yogita Mane. "Multi-class Chest X-ray classification of Pneumonia, Tuberculosis and Normal X-ray images using ConvNets." ITM Web of Conferences 44 (2022): 03007. http://dx.doi.org/10.1051/itmconf/20224403007.

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Pneumonia and Tuberculosis (TB) are two serious and life-threatening diseases that are caused by a bacterial or viral infection of the lungs and have the potential to result in severe consequences within a short period of time. Therefore, early diagnosis is a significant factor in terms of a successful treatment process. Chest X-Rays which are used to diagnose Pneumonia and/or Tuberculosis need expert radiologists for evaluation. Thus, there is a need for an intelligent and automatic system that has the capability of diagnosing chest X-rays, and to simplify the disease detection process for experts and novices. This study aims to develop a model that will help with the classification of chest X-ray medical images into normal vs Pneumonia or Tuberculosis. Medical organizations take a minimum of one day to classify the diagnosis, while our model could perform the same classification within a few seconds. Also, it will display a prediction probability about the predicted class. The model had an accuracy, precision and recall score over 90% which indicates that the model was able to identify patterns. Users can upload their respective chest X-ray image and the model will classify the uploaded image into normal vs abnormal.
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H, Roopa, and Asha T. "Feature Extraction of Chest X-ray Images and Analysis using PCA and kPCA." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 5 (October 1, 2018): 3392. http://dx.doi.org/10.11591/ijece.v8i5.pp3392-3398.

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<p class="abstract">Tuberculosis (TB) is an infectious disease caused by mycobacterium which can be diagnosed by its various symptoms like fever, cough, etc. Tuberculosis can also be analyzed by understanding the chest x-ray of the patient which is revealed by an expert physician .The chest x-ray image contains many features which cannot be directly used by any computer system for analyzing the disease. Features of chest x-ray images must be understood and extracted, so that it can be processed to a form to be fed to any computer system for disease analysis. This paper presents feature extraction of chest x-ray image which can be used as an input for any data mining algorithm for TB disease analysis. So texture and shape based features are extracted from x-ray image using image processing concepts. The features extracted are analyzed using principal component analysis (PCA) and kernel principal component analysis (kPCA) techniques. Filter and wrapper feature selection method using linear regression model were applied on these techniques. The performance of PCA and kPCA are analyzed and found that the accuracy of PCA using wrapper approach is 96.07% when compared to the accuracy of kPCA which is 62.50%. PCA performs well than kPCA with a good accuracy.</p>
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Caseneuve, Guy, Iren Valova, Nathan LeBlanc, and Melanie Thibodeau. "Chest X-Ray Image Preprocessing for Disease Classification." Procedia Computer Science 192 (2021): 658–65. http://dx.doi.org/10.1016/j.procs.2021.08.068.

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Dissertations / Theses on the topic "Chest X-ray image"

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Al-Kabir, Zul Waker Mohammad, and N/A. "A Knowledge Based System for Diagnosis of Lung Diseases from Chest X-Ray Images." University of Canberra. Information Sciences & Engineering, 2007. http://erl.canberra.edu.au./public/adt-AUC20070823.160921.

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The thesis develops a model (that includes a conceptual framework and an implementation) for analysing and classifying traditional X-ray images (MACXI) according to the severity of diseases as a Computer-Aided-Diagnosis tool with three initial objectives. � The first objective was to interpret X-ray images by transferring expert knowledge into a knowledge base (CXKB): to help medical staff to concentrate only on the interest areas of the images. � The second objective was to analyse and classify X-ray images according to the severity of diseases through the knowledge base equipped with an image processor (CXIP). � The third objective was to demonstrate the effectiveness and limitations of several image-processing techniques for analysing traditional chest X-ray images. A database was formed based on collection of expert diagnosis details for lung images. Five important features from lung images, as well as diagnosis rules were identified and simplified. The expert knowledge was transformed into a Knowledge base (KB) for analysing and classifying traditional X-ray images according to the severity of diseases (CXKB). Finally, an image processor named CXIP was developed to extract the features of lung images features and image classification. CXKB contains 63 distinct lung diseases with detailed descriptions. Some 80-chest X-ray images with diagnosis details were collected for the database from different sources, including online medical resources. A total of 61 images were used to determine the important features; 19 chest X-ray images were not used because of low visibility or the difficulty of diagnosis. Finally, only 12 images were selected after examining the diagnosis details, image clarity, image completeness, and image orientation. The most important features of lung diseases are a pattern of lesions with different levels of intensity or brightness. The other major anatomical structures of the chest are the hilum area, the rib area, the trachea area, and the heart area. Seven different severity levels of diseases were determined. Development and simplification of rules based on the image library were analysed, developed, and tested against the 12 images. A level of severity was labelled for each image based on a personal understanding of all the image and diagnosis details. Then, MACXI processed the selected 12 images to determine the level of severity. These 12 images were fed into the CXIP for recognition of the features and classification of the images to an accurate level of severity. Currently, the processor has the ability to identify diseased lung areas with approximately 80% success rate. A step by step demonstration of several image processing techniques that were used to build the processor is given to highlight the effectiveness and limitations of the techniques for analysing traditional chest X-ray images is also presented.
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Wang, Wei. "Image Segmentation Using Deep Learning Regulated by Shape Context." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-227261.

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In recent years, image segmentation by using deep neural networks has made great progress. However, reaching a good result by training with a small amount of data remains to be a challenge. To find a good way to improve the accuracy of segmentation with limited datasets, we implemented a new automatic chest radiographs segmentation experiment based on preliminary works by Chunliang using deep learning neural network combined with shape context information. When the process was conducted, the datasets were put into origin U-net at first. After the preliminary process, the segmented images were then repaired through a new network with shape context information. In this experiment, we created a new network structure by rebuilding the U-net into a 2-input structure and refined the processing pipeline step. In this proposed pipeline, the datasets and shape context were trained together through the new network model by iteration. The proposed method was evaluated on 247 posterior-anterior chest radiographs of public datasets and n-folds cross-validation was also used. The outcome shows that compared to origin U-net, the proposed pipeline reaches higher accuracy when trained with limited datasets. Here the "limited" datasets refer to 1-20 images in the medical image field. A better outcome with higher accuracy can be reached if the second structure is further refined and shape context generator's parameter is fine-tuned in the future.
Under de senaste åren har bildsegmentering med hjälp av djupa neurala nätverk gjort stora framsteg. Att nå ett bra resultat med träning med en liten mängd data kvarstår emellertid som en utmaning. För att hitta ett bra sätt att förbättra noggrannheten i segmenteringen med begränsade datamängder så implementerade vi en ny segmentering för automatiska röntgenbilder av bröstkorgsdiagram baserat på tidigare forskning av Chunliang. Detta tillvägagångssätt använder djupt lärande neurala nätverk kombinerat med "shape context" information. I detta experiment skapade vi en ny nätverkstruktur genom omkonfiguration av U-nätverket till en 2-inputstruktur och förfinade pipeline processeringssteget där bilden och "shape contexten" var tränade tillsammans genom den nya nätverksmodellen genom iteration.Den föreslagna metoden utvärderades på dataset med 247 bröströntgenfotografier, och n-faldig korsvalidering användes för utvärdering. Resultatet visar att den föreslagna pipelinen jämfört med ursprungs U-nätverket når högre noggrannhet när de tränas med begränsade datamängder. De "begränsade" dataseten här hänvisar till 1-20 bilder inom det medicinska fältet. Ett bättre resultat med högre noggrannhet kan nås om den andra strukturen förfinas ytterligare och "shape context-generatorns" parameter finjusteras.
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Kong, Xiang. "Optimization of image quality and minimization of radiation dose for chest computed radiography." Oklahoma City : [s.n.], 2006.

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Mori, Kensaku, Jun-ichi Hasegawa, Yasuhito Suenaga, and Jun-ichiro Toriwaki. "Automated anatomical labeling of the bronchial branch and its application to the virtual bronchoscopy system." IEEE, 2000. http://hdl.handle.net/2237/6872.

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Al-Kabir, Zul Waker Mohammad. "A knowledge based system for diagnosis of lung diseases from chest x-ray images /." Canberra : University of Canberra, 2007. http://erl.canberra.edu.au/public/adt-AUC20070823.160921/index.html.

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Thesis (PhD) -- University of Canberra, 2006.
Thesis submitted in fulfilment of the requirements for the degree of Master of Information Science in the School of Information Sciences and Engineering under the Division of Business, Law and Sciences at the University of Canberra, May 2006. Bibliography: leaves 120-132.
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Радюк, Павло Михайлович, and Pavlo Radiuk. "Інформаційна технологія раннього діагностування пневмонії за індивідуальним підбором параметрів моделі класифікації медичних зображень легень." Дисертація, Хмельницький національний університет, 2021. http://elar.khnu.km.ua/jspui/handle/123456789/11937.

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Дисертаційна робота присвячена розв’язанню актуальної науково-прикладної задачі автоматизації процесу діагностування вірусного пневмонічного запалення за медичними зображеннями легень через розроблення інформаційної технології раннього діагностування пневмонії за індивідуальним підбором параметрів моделі класифікації медичних зображень легень. Застосування розробленої інформаційної технології раннього діагностування пневмонії в клінічній практиці дає змогу підвищити точність та надійність ідентифікації пневмонії на ранніх стадіях за медичними зображеннями грудної клітини людини. Об’єктом дослідження є процес діагностування пневмонії за медичними зображеннями грудної клітини людини. Предметом дослідження є моделі, методи та засоби інформаційної технології для раннього діагностування пневмонії за медичними зображеннями грудної клітини людини. У дисертаційній роботі визначено актуальність застосування інформаційних технологій у галузі цифрового діагностування захворювань легень за медичними зображеннями грудної клітини. На основі проведено аналізу методів та підходів до виявлення пневмонії встановлено, що нейромережеві моделі є найкращим рішенням для розроблення інформаційної технології раннього діагностування. Досліджено методи для налаштування нейромережевої моделі та підходи до пояснення та інтерпретування результатів ідентифікації захворювання легень. За аналізом сучасних підходів, методів та інформаційних технологій для діагностування захворювання легень на ранніх стадіях за медичними зображеннями грудної клітини обґрунтовано потребу в створенні інформаційної технології раннього діагностування пневмонії.
The present thesis is devoted to solving the topical scientific and applied problem of automating the process of diagnosing viral pneumonia by medical images of the lungs through the development of information technology for early diagnosis of pneumonia by the individual selection of parameters of the classification model by medical images of the lungs. Applying the developed information technology for the early diagnosis of pneumonia in clinical practice by medical images of the human chest increases the accuracy and reliability of pneumonia identification in the early stages
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Bustos, Aurelia. "Extraction of medical knowledge from clinical reports and chest x-rays using machine learning techniques." Doctoral thesis, Universidad de Alicante, 2019. http://hdl.handle.net/10045/102193.

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This thesis addresses the extraction of medical knowledge from clinical text using deep learning techniques. In particular, the proposed methods focus on cancer clinical trial protocols and chest x-rays reports. The main results are a proof of concept of the capability of machine learning methods to discern which are regarded as inclusion or exclusion criteria in short free-text clinical notes, and a large scale chest x-ray image dataset labeled with radiological findings, diagnoses and anatomic locations. Clinical trials provide the evidence needed to determine the safety and effectiveness of new medical treatments. These trials are the basis employed for clinical practice guidelines and greatly assist clinicians in their daily practice when making decisions regarding treatment. However, the eligibility criteria used in oncology trials are too restrictive. Patients are often excluded on the basis of comorbidity, past or concomitant treatments and the fact they are over a certain age, and those patients that are selected do not, therefore, mimic clinical practice. This signifies that the results obtained in clinical trials cannot be extrapolated to patients if their clinical profiles were excluded from the clinical trial protocols. The efficacy and safety of new treatments for patients with these characteristics are not, therefore, defined. Given the clinical characteristics of particular patients, their type of cancer and the intended treatment, discovering whether or not they are represented in the corpus of available clinical trials requires the manual review of numerous eligibility criteria, which is impracticable for clinicians on a daily basis. In this thesis, a large medical corpora comprising all cancer clinical trials protocols in the last 18 years published by competent authorities was used to extract medical knowledge in order to help automatically learn patient’s eligibility in these trials. For this, a model is built to automatically predict whether short clinical statements were considered inclusion or exclusion criteria. A method based on deep neural networks is trained on a dataset of 6 million short free-texts to classify them between elegible or not elegible. For this, pretrained word embeddings were used as inputs in order to predict whether or not short free-text statements describing clinical information were considered eligible. The semantic reasoning of the word-embedding representations obtained was also analyzed, being able to identify equivalent treatments for a type of tumor in an analogy with the drugs used to treat other tumors. Results show that representation learning using deep neural networks can be successfully leveraged to extract the medical knowledge from clinical trial protocols and potentially assist practitioners when prescribing treatments. The second main task addressed in this thesis is related to knowledge extraction from medical reports associated with radiographs. Conventional radiology remains the most performed technique in radiodiagnosis services, with a percentage close to 75% (Radiología Médica, 2010). In particular, chest x-ray is the most common medical imaging exam with over 35 million taken every year in the US alone (Kamel et al., 2017). They allow for inexpensive screening of several pathologies including masses, pulmonary nodules, effusions, cardiac abnormalities and pneumothorax. For this task, all the chest-x rays that had been interpreted and reported by radiologists at the Hospital Universitario de San Juan (Alicante) from Jan 2009 to Dec 2017 were used to build a novel large-scale dataset in which each high-resolution radiograph is labeled with its corresponding metadata, radiological findings and pathologies. This dataset, named PadChest, includes more than 160,000 images obtained from 67,000 patients, covering six different position views and additional information on image acquisition and patient demography. The free text reports written in Spanish by radiologists were labeled with 174 different radiographic findings, 19 differential diagnoses and 104 anatomic locations organized as a hierarchical taxonomy and mapped onto standard Unified Medical Language System (UMLS) terminology. For this, a subset of the reports (a 27%) were manually annotated by trained physicians, whereas the remaining set was automatically labeled with deep supervised learning methods using attention mechanisms and fed with the text reports. The labels generated were then validated in an independent test set achieving a 0.93 Micro-F1 score. To the best of our knowledge, this is one of the largest public chest x-ray databases suitable for training supervised models concerning radiographs, and also the first to contain radiographic reports in Spanish. The PadChest dataset can be downloaded on request from http://bimcv.cipf.es/bimcv-projects/padchest/. PadChest is intended for training image classifiers based on deep learning techniques to extract medical knowledge from chest x-rays. It is essential that automatic radiology reporting methods could be integrated in a clinically validated manner in radiologists’ workflow in order to help specialists to improve their efficiency and enable safer and actionable reporting. Computer vision methods capable of identifying both the large spectrum of thoracic abnormalities (and also the normality) need to be trained on large-scale comprehensively labeled large-scale x-ray datasets such as PadChest. The development of these computer vision tools, once clinically validated, could serve to fulfill a broad range of unmet needs. Beyond implementing and obtaining results for both clinical trials and chest x-rays, this thesis studies the nature of the health data, the novelty of applying deep learning methods to obtain large-scale labeled medical datasets, and the relevance of its applications in medical research, which have contributed to its extramural diffusion and worldwide reach. This thesis describes this journey so that the reader is navigated across multiple disciplines, from engineering to medicine up to ethical considerations in artificial intelligence applied to medicine.
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Kitasaka, Takayuki, Kensaku Mori, Jun-ichi Hasegawa, and Jun-ichiro Toriwaki. "A method for automated extraction of aorta and pulmonary artery in the mediastinum using medial line models from 3D chest X-ray CT images without contrast materials." IEEE, 2002. http://hdl.handle.net/2237/6865.

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Oliveira, Ana Luiza da Rosa de. "Avaliação de dose de entrada na pele em pacientes pediátricos através de medidas dosimétricas." Universidade Tecnológica Federal do Paraná, 2008. http://repositorio.utfpr.edu.br/jspui/handle/1/1296.

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A grande utilização de exames de diagnóstico por imagem em crianças trouxe à tona a preocupação com a crescente dose de radiação absorvida na realização de um exame radiográfico. O objetivo desta pesquisa foi realizar avaliação das práticas de raios X na radiologia pediátrica, visando a otimização dos procedimentos radiológicos e a produção de imagens com qualidade para o diagnóstico com a menor dose ao paciente. A metodologia foi baseada no acompanhamento de exames pediátricos e medidas dosimétricas através do uso de dosímetros termoluminescentes TLDs e software específico (DoseCal) para a constatação da realidade dos serviços de radiologia pediátrica. Medidas de pacientes pediátricos em exames radiográficos de tórax foram realizadas em um hospital público de Curitiba e em uma clínica em Cascavel. Grupos com diferentes faixas etárias foram formados na avaliação de exames rotineiros de tórax nas projeções AP/PA e LAT, e ossos da face na projeção lateral, onde foram divididos em grupos de 0-1 ano, 1-5 anos, 5-10 anos e 10-15 anos. As doses obtidas através do software DoseCal foram comparadas entre si para determinar sua variabilidade. A DEP determinada pelos TLDs foi comparada com os valores de referência dados pela comunidade européia para verificar as doses utilizadas. Os valores de dose para crianças de até 1 ano apresentaram-se altos em comparação com os demais grupos avaliados, um fator justificado em partes pela limitação dos equipamentos utilizados. Na radiologia convencional os valores obtidos através dos TLDs foram satisfatórios, obedecendo a referência máxima descrita pela comissão européia. Na radiologia digital indireta obtivemos valores acima dos referenciados, fator este resultante da implantação e da adaptação das técnicas radiológicas a nova forma de captação de imagem. Concluí-se que o aprimoramento técnico das equipes em radiologia pediátrica é uma das melhores maneiras de se obter bons resultados na diminuição da dose.
The great use of examinations of diagnosis for image in children brought the concern with the increasing dose of radiation absorbed in the accomplishment of a radiographic examination. The objective of this research is to carry through evaluation of the practical ones of x-rays in pediatric radiology, aiming at to optimize the radiological rocedures and the production of images with quality for the diagnosis with the lesser dose to the patient. The methodology is based on the accompaniment of pediatric examinations and dosimetry measures through the use of dosemeters TLD and specific software (DoseCal) for the evidence of the reality in a radiology service. Measures of pediatric patients in radiographic examinations of thorax had been carried through in a public hospital in the Curitiba. Groups with different age groups had been formed in the evaluation of routine examinations of thorax in projections AP/PA and LAT, where they are divided in groups of 0-1 year, 1-5 years, 5-10 years and 10-15 years. Part of the carried through examinations had been evaluated with thermoluminescence dosemeters TLD-100 for the collection of the entrance surface dose (ESD). The measured doses are compared with the gotten ones with the DoseCal software, that makes the calculation of dose for each patient from the income of the device of rays X. The ESD is evaluated always that it has diagnostic quality in the radiographic image. The objective is to verify if the minimum requirements had been reached, for a good quality of image and bringing a small dose to the patient, as party to suit of to optimize procedures.
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HSU, YI-YU, and 許億裕. "Convolutional Neural Network Technology Applied to Chest X-ray Image Recognition." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/mfd77z.

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碩士
輔仁大學
統計資訊學系應用統計碩士在職專班
107
In recent years, the expeditious development of Artificial intelligence penetrates into all fields. We discover the application of artificial intelligence in every field, and in-depth learning is an emerging technology which has received much attention in recent years. It is a very important part in the field of medical image recognition. The convolutional neural network algorithm has achieved excellent results in the field of image recognition. However, the superparametric configuration rules of CNN, it is still time-consuming to rely on data scientists constantly adjusting the test during the modeling process. In this study, convolutional neural network and machine learning KNN algorithm are used to compare the chest X-ray images. The results show that the accuracy of CNN algorithm is better than that of KNN algorithm. In future, the original model needs to be retrained because of the increase of image samples. As a result, the original model needs to be retrained, so that analysts can speed up the retraining of the model and quickly find stable and efficient models. On the other hand, it is also hoped to find out the difficulties that may be encountered in the application of CNN algorithm in image recognition, for those who are interested in engaging in relevant research in the future.
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Books on the topic "Chest X-ray image"

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Goodman, Lawrence R. Imaging the respiratory system in the critically ill. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199600830.003.0078.

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Routine radiographs are not cost effective in the intensive care unit (ICU) setting. Most published guidelines agree that radiographs are worthwhile after insertion of tubes or catheters, and in patients receiving mechanical ventilation. Otherwise, they are required only for change in the patient’s clinical status. Picture archiving and communication systems utilize digital imaging technology. They provide superior quality images, rapid image availability at multiple sites, and fewer repeat examinations, reducing both cost and patient radiation. Disadvantages of picture archiving and communication systems include expensive equipment and personnel required to keep them functioning. The majority of chest X-ray abnormalities in the ICU are best understood by paying careful attention to the initial appearance of the X-ray in relation to the patient’s onset of symptoms and the progression of abnormalities over the next few days.
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Parkhomenko, Alexander, Olga S. Gurjeva, and Tetyana Yalynska. Clinical assessment and monitoring of chest radiographs. Oxford University Press, 2015. http://dx.doi.org/10.1093/med/9780199687039.003.0019.

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This chapter reviews the main problems in obtaining portable X-rays in intensive cardiac care unit patients and describes specific features of radiographs taken in the supine anteroposterior position. It also includes a brief review of a systematic, multistep approach of evaluating the quality of radiographic images and describing the chest wall, pulmonary vasculature, the heart and its chambers, the great vessels, and the position of tubes, lines, and devices. This chapter covers the most common conditions for which chest radiographs are useful and provides intensive cardiac care unit physicians, cardiologists, cardiology fellows, and medical students with basic information on water retention, air collection, and lung-related problems. It also focuses on the monitoring of line and device placements (e.g. central venous catheters, tube malposition) and procedure-related abnormalities, which may be apparent on chest X-rays and are helpful for timely diagnoses.
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Parkhomenko, Alexander, Olga S. Gurjeva, and Tetyana Yalynska. Clinical assessment and monitoring of chest radiographs. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199687039.003.0019_update_001.

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This chapter reviews the main problems in obtaining portable X-rays in intensive cardiac care unit patients and describes specific features of radiographs taken in the supine anteroposterior position. It also includes a brief review of a systematic, multistep approach of evaluating the quality of radiographic images and describing the chest wall, pulmonary vasculature, the heart and its chambers, the great vessels, and the position of tubes, lines, and devices. This chapter covers the most common conditions for which chest radiographs are useful and provides intensive cardiac care unit physicians, cardiologists, cardiology fellows, and medical students with basic information on water retention, air collection, and lung-related problems. It also focuses on the monitoring of line and device placements (e.g. central venous catheters, tube malposition) and procedure-related abnormalities, which may be apparent on chest X-rays and are helpful for timely diagnoses.
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Parkhomenko, Alexander, Olga S. Gurjeva, and Tetyana Yalynska. Clinical assessment and monitoring of chest radiographs. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199687039.003.0019_update_002.

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This chapter reviews the main problems in obtaining portable X-rays in intensive cardiac care unit patients and describes specific features of radiographs taken in the supine anteroposterior position. It also includes a brief review of a systematic, multistep approach of evaluating the quality of radiographic images and describing the chest wall, pulmonary vasculature, the heart and its chambers, the great vessels, and the position of tubes, lines, and devices. This chapter covers the most common conditions for which chest radiographs are useful and provides intensive cardiac care unit physicians, cardiologists, cardiology fellows, and medical students with basic information on water retention, air collection, and lung-related problems. It also focuses on the monitoring of line and device placements (e.g. central venous catheters, tube malposition) and procedure-related abnormalities, which may be apparent on chest X-rays and are helpful for timely diagnoses.
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5

Parkhomenko, Alexander, Olga S. Gurjeva, and Tetyana Yalynska. Clinical assessment and monitoring of chest radiographs. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780199687039.003.0019_update_003.

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This chapter reviews the main problems in obtaining portable X-rays in intensive cardiac care unit patients and describes specific features of radiographs taken in the supine anteroposterior position. It also includes a brief review of a systematic, multistep approach of evaluating the quality of radiographic images and describing the chest wall, pulmonary vasculature, the heart and its chambers, the great vessels, and the position of tubes, lines, and devices. This chapter covers the most common conditions for which chest radiographs are useful and provides intensive cardiac care unit physicians, cardiologists, cardiology fellows, and medical students with basic information on water retention, air collection, and lung-related problems. It also focuses on the monitoring of line and device placements (e.g. central venous catheters, tube malposition) and procedure-related abnormalities, which may be apparent on chest X-rays and are helpful for timely diagnoses.
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Book chapters on the topic "Chest X-ray image"

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Neves, João, Ricardo Faria, Victor Alves, Filipa Ferraz, Henrique Vicente, and José Neves. "Chest X-Ray Image Analysis." In Lecture Notes in Computer Science, 48–61. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93581-2_3.

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Najdenkoska, Ivona, Xiantong Zhen, Marcel Worring, and Ling Shao. "Variational Topic Inference for Chest X-Ray Report Generation." In Medical Image Computing and Computer Assisted Intervention – MICCAI 2021, 625–35. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87199-4_59.

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Vinhais, Carlos, and Aurélio Campilho. "Optimal Detection of Symmetry Axis in Digital Chest X-ray Images." In Pattern Recognition and Image Analysis, 1082–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-44871-6_125.

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Santosh, K. C., and Laurent Wendling. "Automated Chest X-ray Image View Classification using Force Histogram." In Communications in Computer and Information Science, 333–42. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-4859-3_30.

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Verma, Parag, Ankur Dumka, Alaknanda Ashok, Amit Dumka, and Anuj Bhardwaj. "Chest X-Ray Image-Based Testing Using Machine Learning Techniques." In Covid-19, 241–78. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003131410-6.

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Syeda-Mahmood, Tanveer, Ken C. L. Wong, Yaniv Gur, Joy T. Wu, Ashutosh Jadhav, Satyananda Kashyap, Alexandros Karargyris, et al. "Chest X-Ray Report Generation Through Fine-Grained Label Learning." In Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 561–71. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59713-9_54.

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Hou, Benjamin, Georgios Kaissis, Ronald M. Summers, and Bernhard Kainz. "RATCHET: Medical Transformer for Chest X-ray Diagnosis and Reporting." In Medical Image Computing and Computer Assisted Intervention – MICCAI 2021, 293–303. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87234-2_28.

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Mata, Diogo, Wilson Silva, and Jaime S. Cardoso. "Increased Robustness in Chest X-Ray Classification Through Clinical Report-Driven Regularization." In Pattern Recognition and Image Analysis, 119–28. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04881-4_10.

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Mata, Diogo, Wilson Silva, and Jaime S. Cardoso. "Increased Robustness in Chest X-Ray Classification Through Clinical Report-Driven Regularization." In Pattern Recognition and Image Analysis, 119–28. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04881-4_10.

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Chandra, Tej Bahadur, and Kesari Verma. "Pneumonia Detection on Chest X-Ray Using Machine Learning Paradigm." In Proceedings of 3rd International Conference on Computer Vision and Image Processing, 21–33. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-32-9088-4_3.

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Conference papers on the topic "Chest X-ray image"

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Xue, Zhiyun, Daekeun You, Sema Candemir, Stefan Jaeger, Sameer Antani, L. Rodney Long, and George R. Thoma. "Chest X-ray Image View Classification." In 2015 IEEE 28th International Symposium on Computer-Based Medical Systems (CBMS). IEEE, 2015. http://dx.doi.org/10.1109/cbms.2015.49.

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Kittiworapanya, Phongsathorn, and Kitsuchart Pasupa. "An Image Segment-based Classification for Chest X-Ray Image." In CSBio2020: The 11th International Conference on Computational Systems-Biology and Bioinformatics. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3429210.3429227.

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Majdi, Mohammad S., Khalil N. Salman, Michael F. Morris, Nirav C. Merchant, and Jeffrey J. Rodriguez. "Deep Learning Classification of Chest X-Ray Images." In 2020 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI). IEEE, 2020. http://dx.doi.org/10.1109/ssiai49293.2020.9094612.

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Moradi, Mehdi, Ali Madani, Alexandros Karargyris, and Tanveer F. Syeda-Mahmood. "Chest x-ray generation and data augmentation for cardiovascular abnormality classification." In Image Processing, edited by Elsa D. Angelini and Bennett A. Landman. SPIE, 2018. http://dx.doi.org/10.1117/12.2293971.

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Degerli, Aysen, Mete Ahishali, Serkan Kiranyaz, Muhammad E. H. Chowdhury, and Moncef Gabbouj. "Reliable Covid-19 Detection using Chest X-Ray Images." In 2021 IEEE International Conference on Image Processing (ICIP). IEEE, 2021. http://dx.doi.org/10.1109/icip42928.2021.9506442.

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Tran, Minh-Trieu, Soo-Hyung Kim, Hyung-Jeong Yang, and Guee-Sang Lee. "Deep Learning-Based Inpainting for Chest X-ray Image." In SMA 2020: The 9th International Conference on Smart Media and Applications. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3426020.3426088.

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Nugroho, Bayu Adhi. "An Improved Algorithm for Chest X-Ray Image Classification." In 2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI). IEEE, 2021. http://dx.doi.org/10.1109/isriti54043.2021.9702770.

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Martins, Roberto Augusto Philippi, and Danilo Silva. "On Teacher-Student Semi-Supervised Learning for Chest X-ray Image Classification." In Congresso Brasileiro de Inteligência Computacional. SBIC, 2021. http://dx.doi.org/10.21528/cbic2021-80.

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The lack of labeled data is one of the main prohibiting issues on the development of deep learning models, as they rely on large labeled datasets in order to achieve high accuracy in complex tasks. Our objective is to evaluate the performance gain of having additional unlabeled data in the training of a deep learning model when working with medical imaging data. We present a semi-supervised learning algorithm that utilizes a teacher-student paradigm in order to leverage unlabeled data in the classification of chest X-ray images. Using our algorithm on the ChestX-ray14 dataset, we manage to achieve a substantial increase in performance when using small labeled datasets. With our method, a model achieves an AUROC of 0.822 with only 2% labeled data and 0.865 with 5% labeled data, while a fully supervised method achieves an AUROC of 0.807 with 5% labeled data and only 0.845 with 10%.
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Wong, Weichieh, Ahmad Adel Abu-Shareha, Muhammad Fermi Pasha, and Rajeswari Mandava. "Enhanced local binary pattern for chest X-ray classification." In 2013 IEEE Second International Conference on Image Information Processing (ICIIP). IEEE, 2013. http://dx.doi.org/10.1109/iciip.2013.6707597.

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Ponomaryov, Volodymyr I., Jose A. Almaraz-Damian, Rogelio Reyes-Reyes, and Clara Cruz-Ramos. "Chest x-ray classification using transfer learning on multi-GPU." In Real-Time Image Processing and Deep Learning 2021, edited by Nasser Kehtarnavaz and Matthias F. Carlsohn. SPIE, 2021. http://dx.doi.org/10.1117/12.2587537.

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