Relevant bibliographies by topics / LUNG PATTERN CLASSIFICATION

Academic literature on the topic 'LUNG PATTERN CLASSIFICATION'

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Published: 11 September 2023

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Journal articles on the topic "LUNG PATTERN CLASSIFICATION"

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Mangayarkarasi, T., R. Chithrakkannan, and R. Karthikeyan. "PATTERN KNOWLEDGE DISCOVERY BASED LUNG CANCER CLASSIFICATION SYSTEM." CARDIOMETRY, no. 26 (March 1, 2023): 623–28. http://dx.doi.org/10.18137/cardiometry.2023.26.623628.

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An accurate detection of abnormal lung nodule detection is very important for effective treatment and surgical procedure to remove the nodules. This paper introduces an efficient deep learning model to classify lung cancer in both left and right lung. It consists of three important stages; preprocessing, lung region detection and abnormal lung nodule detection. Further, a detailed discussion about the performance of the system is given using two benchmark databases; 30 lung CT images taken from the ELCAP dataset and 130 lung CT images taken from the LIDC dataset. An algorithmic framework is first created for the purpose of segmenting left and right lung region by a morphological algorithm after removing the noise by a wiener filter. A well defined deep learning architecture is designed for effective classification or detection of abnormal lung nodule detection by semantic classification. The proposed system is validated on LIDC and ELCAP database and provides an average accuracy of 97.86%.
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Ajin, M., and L. Mredhula. "Diagnosis Of Interstitial Lung Disease By Pattern Classification." Procedia Computer Science 115 (2017): 195–208. http://dx.doi.org/10.1016/j.procs.2017.09.126.

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Boukansa, Sara, Zineb Benbrahim, Sanaa Gamrani, Sanae Bardai, Laila Bouguenouch, Asmae Mazti, Nadia Boutahiri, et al. "Correlation of Epidermal Growth Factor Receptor Mutation With Major Histologic Subtype of Lung Adenocarcinoma According to IASLC/ATS/ERS Classification." Cancer Control 29 (January 2022): 107327482210849. http://dx.doi.org/10.1177/10732748221084930.

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Objective Our prospective study aims to define the correlation of EGFR(epidermal growth factor receptor) mutations with major histological subtypes of lung adenocarcinoma from resected and non-resected specimens, according to the WHO 2015 classification, in Moroccan North East Population. Methods Epidermal growth factor receptor mutations of 150 primary lung adenocarcinoma were performed using Real-Time PCR or SANGER sequencing. SPSS 21 was used to assess the relationship between histological subtypes of lung adenocarcinoma and EGFR mutation status. Results 25 mutations were detected in the series of 150 lung adenocarcinomas, most of which were found in cases with papillary, acinar, patterns than without these patterns and more frequently occurred in the cases without solid pattern than with this pattern. A significant correlation was observed between EGFR mutation and acinar (P = 0,024), papillary pattern (P = 0,003) and, negative association with a solid pattern (P < 0,001). In females, EGFR mutations were significantly correlated with the acinar pattern (P = 0,02), whereas in males with the papillary pattern (P = 0,01). Association between the histologic component and exon 19 deletions and exon 21 mutations were also evaluated and, we found a significant correlation between the papillary major pattern with exon 19 mutations (P = 0,004) and, ex21 with the acinar component (P = 0,03). Conclusion An analysis of resected and non-resected lung ADC specimens in 150 Moroccan Northeast patients, revealed that acinar and papillary patterns may predict the presence of a mutation in the EGFR gene. While the solid major pattern may indicate a low mutation rate of the EGFR gene.
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SHAMSHEYEVA, ALENA, ARCOT SOWMYA, and PETER WILSON. "SEGMENTATION OF LUNG PATTERNS IN HIGH-RESOLUTION COMPUTED TOMOGRAPHY IMAGES OF THE LUNG." International Journal of Computational Intelligence and Applications 07, no. 03 (September 2008): 265–80. http://dx.doi.org/10.1142/s1469026808002259.

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An automated method is presented for segmentation of two-dimensional HRCT images of the lung into regions of four lung patterns: normal, emphysema, honeycombing, and ground-glass opacity (GGO). Segmentation was implemented in two stages. At the first stage, pixel-wise classification of the lung area was performed using local textural features extracted by the wavelet transform. At the second stage, classification results were refined by application of knowledge-based rules. Performance of the method was compared on two sets of HRCT images: one included HRCT images with characteristic examples of lung patterns and the other consisted of unselected HRCT images that represented a model of routine operations at a general radiology practice. On the first set of images, sensitivity of the method ranged from 0.92 to 0.99, and specificity ranged from 0.96 to 0.99. On the second set of images, sensitivity and specificity were, respectively, 0.49 and 0.95 for emphysema, 0.87 and 0.55 for normal, 0.34 and 0.99 for honeycombing, and 0.57 and 0.94 for GGO. The two-stage approach allowed for simple and effective application of high-level knowledge about appearance of lung patterns on HRCT images and did not require feature and region of interest size selection for the first stage of pixel-wise lung pattern classification.
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Subramaniam, Umashankar, M. Monica Subashini, Dhafer Almakhles, Alagar Karthick, and S. Manoharan. "An Expert System for COVID-19 Infection Tracking in Lungs Using Image Processing and Deep Learning Techniques." BioMed Research International 2021 (November 13, 2021): 1–17. http://dx.doi.org/10.1155/2021/1896762.

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The proposed method introduces algorithms for the preprocessing of normal, COVID-19, and pneumonia X-ray lung images which promote the accuracy of classification when compared with raw (unprocessed) X-ray lung images. Preprocessing of an image improves the quality of an image increasing the intersection over union scores in segmentation of lungs from the X-ray images. The authors have implemented an efficient preprocessing and classification technique for respiratory disease detection. In this proposed method, the histogram of oriented gradients (HOG) algorithm, Haar transform (Haar), and local binary pattern (LBP) algorithm were applied on lung X-ray images to extract the best features and segment the left lung and right lung. The segmentation of lungs from the X-ray can improve the accuracy of results in COVID-19 detection algorithms or any machine/deep learning techniques. The segmented lungs are validated over intersection over union scores to compare the algorithms. The preprocessed X-ray image results in better accuracy in classification for all three classes (normal/COVID-19/pneumonia) than unprocessed raw images. VGGNet, AlexNet, Resnet, and the proposed deep neural network were implemented for the classification of respiratory diseases. Among these architectures, the proposed deep neural network outperformed the other models with better classification accuracy.
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Anthimopoulos, Marios, Stergios Christodoulidis, Lukas Ebner, Andreas Christe, and Stavroula Mougiakakou. "Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network." IEEE Transactions on Medical Imaging 35, no. 5 (May 2016): 1207–16. http://dx.doi.org/10.1109/tmi.2016.2535865.

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Ummay Atiya, S., and N. V.K Ramesh. "Pattern classification of interstitial lung disease in high resolution clinical datasets: A systematic review." International Journal of Engineering & Technology 7, no. 2.7 (March 18, 2018): 114. http://dx.doi.org/10.14419/ijet.v7i2.7.10275.

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Automated tissues characterization helps to diagnosis the various diseases including Interstitial lung diseases (ILD). The various features and the several classifiers are used in categorize the different layers depend on the pattern presented in the image. The different types of diseases may occur in the lungs and some of the diseases happen to leave the scars. These scars can be found in the High Resolution Computed Tomography (HRCT) and have different pattern. The different diseases cause the different pattern in the images and these is classified using the efficient classifier that helps to diagnosis the diseases. In this paper, review for the many researches regarding to the classification of the different pattern from the Computed Tomography (CT) images is presented. The evaluation of the efficiency of the methods in terms of classifier and database used for the research is made. The Deep Convolution Neural Network (CNN) provides the promising classifier efficiency compared to the other researches for different pattern. In general, there are five types of pattern is classified: Healthy, ground glass, honeycomb, Fibrosis, and emphysema.
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Raparia, Kirtee, and Rishi Raj. "Tissue Continues to Be the Issue: Role of Histopathology in the Context of Recent Updates in the Radiologic Classification of Interstitial Lung Diseases." Archives of Pathology & Laboratory Medicine 143, no. 1 (January 1, 2019): 30–33. http://dx.doi.org/10.5858/arpa.2018-0134-ra.

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Context.— High-resolution computed tomography (HRCT) imaging has an increasingly important role in clinical decision-making in patients with interstitial lung diseases. The recent Fleischner Society white paper on the diagnostic criteria for idiopathic pulmonary fibrosis highlights the advances in our understanding of HRCT imaging in interstitial lung diseases. Objective.— To discuss the evidence and recommendations outlined in the white paper as it pertains to the radiologic diagnosis of interstitial lung disease, specifically highlighting the current limitations of HRCT in confidently predicting histopathologic findings. Data Sources.— The recent Fleischner Society white paper and other studies pertaining to the role of HRCT in predicting histopathology in interstitial lung diseases are reviewed. Conclusions.— High-resolution computed tomography is highly predictive of a usual interstitial pneumonia (UIP) pattern on histopathology when the HRCT shows a typical UIP pattern on a “confident” read by the radiologist. A probable UIP pattern is also very predictive of a UIP pattern on histopathology, and histopathologic confirmation is not needed for most patients demonstrating this pattern in the appropriate clinical setting. A UIP pattern may be seen in a substantial proportion of patients with an “indeterminate UIP” pattern on HRCT and in many patients for whom the HRCT suggests an alternative diagnosis; histopathologic confirmation should be considered in patients demonstrating these patterns whenever feasible.
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Khoiro, M., R. A. Firdaus, E. Suaebah, M. Yantidewi, and Dzulkiflih. "Segmentation Effect on Lungs X-Ray Image Classification Using Convolution Neural Network." Journal of Physics: Conference Series 2392, no. 1 (December 1, 2022): 012024. http://dx.doi.org/10.1088/1742-6596/2392/1/012024.

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Abstract The effect of segmentation on lung X-ray image classification has been analyzed in this study. The 150 lung x-ray images in this study were separated into 78 as training data, 30 as validation data, and 42 as testing in three categories: normal lungs, effusion lungs, and cancer lungs. In pre-processing, the images were modified by adaptive histogram equalization to improve image quality and increase image contrast. The segmentation aims to mark the image by contouring the lung area obtained from the thresholding and some morphological manipulation processes such as filling holes, area openings, and labelling. Image classification uses Convolutional Neural Network (CNN) with five convolution layers, an Adam optimizer, and 30 epochs. The segmentation effect is analyzed by comparing the classification performance of the segmented and unsegmented images. In the study, the unsegmented X-ray image dataset classification reached an overall accuracy of 59.52% in the network testing process. The segmented X-ray image dataset obtained greater accuracy, 73.81%. It indicated that the segmentation process could improve network performance because the input pattern of the segmented image is easier to classify. Furthermore, the segmentation technique in the study can be one of the alternatives to developing image classification technologies, especially for medical image diagnosis. Segmentation Effect on Lungs X-Ray Image Classification Using Convolution Neural Network.
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Vasconcelos, Verónica, João Barroso, Luis Marques, and José Silvestre Silva. "Enhanced Classification of Interstitial Lung Disease Patterns in HRCT Images Using Differential Lacunarity." BioMed Research International 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/672520.

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The analysis and interpretation of high-resolution computed tomography (HRCT) images of the chest in the presence of interstitial lung disease (ILD) is a time-consuming task which requires experience. In this paper, a computer-aided diagnosis (CAD) scheme is proposed to assist radiologists in the differentiation of lung patterns associated with ILD and healthy lung parenchyma. Regions of interest were described by a set of texture attributes extracted using differential lacunarity (DLac) and classical methods of statistical texture analysis. The proposed strategy to compute DLac allowed a multiscale texture analysis, while maintaining sensitivity to small details. Support Vector Machines were employed to distinguish between lung patterns. Training and model selection were performed over a stratified 10-fold cross-validation (CV). Dimensional reduction was made based on stepwise regression (F-test,pvalue < 0.01) during CV. An accuracy of 95.8 ± 2.2% in the differentiation of normal lung pattern from ILD patterns and an overall accuracy of 94.5 ± 2.1% in a multiclass scenario revealed the potential of the proposed CAD in clinical practice. Experimental results showed that the performance of the CAD was improved by combining multiscale DLac with classical statistical texture analysis.
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Dissertations / Theses on the topic "LUNG PATTERN CLASSIFICATION"

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Froz, Bruno Rodrigues. "CLASSIFICAÇÃO DE NÓDULOS PULMONARES UTILIZANDO VIDAS ARTIFICIAIS, MVS E MEDIDAS DIRECIONAIS DE TEXTURA." Universidade Federal do Maranhão, 2015. http://tedebc.ufma.br:8080/jspui/handle/tede/285.

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Made available in DSpace on 2016-08-17T14:52:36Z (GMT). No. of bitstreams: 1 dissertacao Bruno Rodrigues Froz.pdf: 1583465 bytes, checksum: f53ff1f85d91788fc7d52925b16f6794 (MD5) Previous issue date: 2015-02-02
Conselho Nacional de Desenvolvimento Científico e Tecnológico
The lung cancer is known for presenting the highest mortality rate and one of the lowest survival rate after diagnosis, which is mainly caused by the late detection and treatment. With the goal of assist the lung cancer specialists, computed aided diagnosis systems are developed to automate the detection and diagnosis of this disease. This work proposes a methodology to classify, with computed tomography images, lung nodules candidates and non-nodules candidates. The Lung Image Database Consortium (LIDC) image database is used to create an image database with nodules candidates and an image database with non-nodule candidates. Three techniques are utilized to extract texture measurements. The first one is the artificial life algorithm Artificial Crawlers. The second one is the use of Rose Diagram to extract directional measurements. The third and last one is an hybrid model to join the Artificial Crawlers and Rose Diagram texture measurements. In the classification, que Support Vector Machine classifier is used, with its radial basis kernel. The archived results are very promising. With 833 LIDC exams, divided in 60% for train and 40% for test, we reached na accuracy mean of 94,30%, sensitivity mean of 91,86%, specificity mean of 94,78%, variance coefficient of accuracy of 1,61% and ROC curves mean área of 0,922.
O câncer de pulmão é conhecido por apresentar a maior taxa de mortalidade e uma das menores taxas de sobrevida após o diagnóstico, o que é causado principalmente pela detecção e tratamento tardios. Para o auxílio dos especialistas em câncer pulmonar, são desenvolvidos sistemas de diagnósticos auxiliados por computador com o objetivo de automatizar a detecção e diagnóstico dessa doença. Este trabalho propõe uma metodologia para a classificação, através de imagens de tomografias computadorizadas, de candidatos a nódulos pulmonares e candidatos a não-nódulos. O banco de imagens Lung Image Database Consortium (LIDC) é utilizado para a criação de uma base de imagens de candidatos a nódulos e uma base de imagens de candidatos a não-nódulos. Três técnicas são utilizadas para a extração de medidas de textura. A primeira delas é o algoritmo de vidas artificiais Artificial Crawlers. A segunda técnica é a utilização do Rose Diagram para a extração de medidas direcionais. A terceira e última técnica é um modelo híbrido que une as medidas do Artificial Crawlers e do Rose Diagram. Para a classificação é utilizado o classificador Máquina de Vetor de Suporte (MVS), com o kernel de base radial. Os resultados alcançados são muito promissores. Utilizando 833 exames do LIDC divididos em 60% para treino e 40% para teste, alcançou-se uma média de acurácia de 94,30%, média de sensibilidade de 91,86%, média de especificidade de 94,78%, coeficiente de variância da acurácia de 1,61% e área média das curvas ROC de 0,922.
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PODDAR, KRITI. "IMPROVED LUNG PATTERN CLASSIFICATION FOR INTERSTITIAL LUNG DISEASE USING DEEP LEARNING." Thesis, 2019. http://dspace.dtu.ac.in:8080/jspui/handle/repository/16845.

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In this research work, methods for classification and characterization of a computer aided diagnosis (CAD) system for interstitial lung disease have been executed. Although a lot of research has already been done in this regard, the growing popularity of the Deep learning techniques have evoked expectations that they might be applied in the field of image analysis as well. In the present work, a lot of methodologies have been applied on the chosen dataset and their results have been evaluated post which a network has been proposed. It consists of a layer of segmentation deploying the UNET architecture which is then connected and followed by a layer similar to that of CNN but with little modifications. It essentially consists of 5 convolutional layers and ReLU activations, followed by maxpooling which is then followed by 3 fully connected layers and a softmax layer. The last dense layer has got 5 outputs which form the classes to be considered. These are: healthy, ground glass opacity, emphysema, fibrosis and micronodules. In order to train and evaluate various network methodologies, a dataset of approximately 18400 image patches have been taken. A comparative analysis established the effectiveness of the proposed framework against all the other methods in a challenging dataset.
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Chen, Pu. "Classification tree models for predicting cancer status." 2009. http://digital.library.duq.edu/u?/etd,109505.

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Books on the topic "LUNG PATTERN CLASSIFICATION"

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Denton, Christopher P., and Pia Moinzadeh. Systemic sclerosis. Oxford University Press, 2013. http://dx.doi.org/10.1093/med/9780199642489.003.0121.

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The term 'scleroderma' describes a group of conditions in which the development of thickened, fibrotic skin is a cardinal feature. This includes localized forms of scleroderma (e.g. morphoea) and also systemic forms of the disease that are more correctly termed systemic sclerosis. Systemic sclerosis (SSc) is a multiorgan, autoimmune disease that has a high clinical burden and mortality, due to affecting the skin as well as internal organs. As with other related diseases there is a female predominance and marked clinical diversity. The pathogenesis of SSc is not fully elucidated; it includes endothelial cell injury fibroblast activation and autoimmunity that lead to skin and internal organ manifestations. The majority of cases exhibit characteristic serum autoantibodies. Some of these antibodies are scleroderma-specific reactivities including anti-centromere (ACA), anti-topoisomerase-1 (ATA or Scl 70) or anti-RNA polymerase III antibodies. These anti-nuclear antibody (ANA) patterns are generally mutually exclusive and serve as useful clinical markers of disease subgroups. Additional subsetting of scleroderma cases, based on the extent of skin sclerosis, permits classification into limited and diffuse subsets. Because of the heterogeneity of the disease patients may suffer from different organ manifestations, such as lung fibrosis, hypertensive renal crisis, severe cardiac disease, gastrointestinal involvement, and pulmonary arterial hypertension. Although outcomes have improved recently, systemic sclerosis still has the highest case-specific mortality of any of the autoimmune rheumatic diseases and requires careful and systematic investigation, management and follow-up. Treatment includes symptomatic strategies with attention to each involved organ system; it is still an area where therapeutic progress and better understanding of pathogenesis is increasingly anticipated.
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Book chapters on the topic "LUNG PATTERN CLASSIFICATION"

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Wong, James S. J., and Tatjana Zrimec. "Classification of Lung Disease Pattern Using Seeded Region Growing." In Lecture Notes in Computer Science, 233–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11941439_27.

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Bobadilla, Julio Cesar Mendoza, and Helio Pedrini. "Lung Nodule Classification Based on Deep Convolutional Neural Networks." In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 117–24. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-52277-7_15.

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Blatt, Rossella, Andrea Bonarini, and Matteo Matteucci. "Pattern Classification Techniques for Lung Cancer Diagnosis by an Electronic Nose." In Computational Intelligence in Healthcare 4, 397–423. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14464-6_18.

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Ramos, Bernardo, Tania Pereira, Francisco Silva, José Luis Costa, and Hélder P. Oliveira. "Differential Gene Expression Analysis of the Most Relevant Genes for Lung Cancer Prediction and Sub-type Classification." In Pattern Recognition and Image Analysis, 182–91. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04881-4_15.

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Cardoso, Isadora, Eliana Almeida, Héctor Allende-Cid, Alejandro C. Frery, Rangaraj M. Rangayyan, Paulo M. Azevedo-Marques, and Heitor S. Ramos. "Evaluation of Deep Feedforward Neural Networks for Classification of Diffuse Lung Diseases." In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 152–59. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-75193-1_19.

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Nascimento, Leonardo Barros, Anselmo Cardoso de Paiva, and Aristófanes Corrêa Silva. "Lung Nodules Classification in CT Images Using Shannon and Simpson Diversity Indices and SVM." In Machine Learning and Data Mining in Pattern Recognition, 454–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31537-4_36.

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Alsubaie, Najah, Muhammad Shaban, David Snead, Ali Khurram, and Nasir Rajpoot. "A Multi-resolution Deep Learning Framework for Lung Adenocarcinoma Growth Pattern Classification." In Communications in Computer and Information Science, 3–11. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95921-4_1.

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da Silva, Cleriston Araujo, Aristófanes Corrêa Silva, Stelmo Magalhães Barros Netto, Anselmo Cardoso de Paiva, Geraldo Braz Junior, and Rodolfo Acatauassú Nunes. "Lung Nodules Classification in CT Images Using Simpson’s Index, Geometrical Measures and One-Class SVM." In Machine Learning and Data Mining in Pattern Recognition, 810–22. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03070-3_61.

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Vo, Kiet T., and Arcot Sowmya. "Directional Multi-scale Modeling of High-Resolution Computed Tomography (HRCT) Lung Images for Diffuse Lung Disease Classification." In Computer Analysis of Images and Patterns, 663–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03767-2_81.

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Sørensen, Lauge, Saher B. Shaker, and Marleen de Bruijne. "Texture Classification in Lung CT Using Local Binary Patterns." In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2008, 934–41. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-85988-8_111.

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Conference papers on the topic "LUNG PATTERN CLASSIFICATION"

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He, Jing Selena, Meng Han, Lei Yu, and Chao Mei. "Lung Pattern Classification Via DCNN." In 2020 IEEE International Conference on Big Data (Big Data). IEEE, 2020. http://dx.doi.org/10.1109/bigdata50022.2020.9378090.

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Priya, C. Lakshmi, D. Gowthami, and S. Poonguzhali. "Lung pattern classification for interstitial lung diseases using an ANN-back propagation network." In 2017 International Conference on Communication and Signal Processing (ICCSP). IEEE, 2017. http://dx.doi.org/10.1109/iccsp.2017.8286732.

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Zahari, Rahimi, Julie Cox, and Boguslaw Obara. "Quantifying the Uncertainty in 3D CT Lung Cancer Images Classification." In 2023 IEEE 13th International Conference on Pattern Recognition Systems (ICPRS). IEEE, 2023. http://dx.doi.org/10.1109/icprs58416.2023.10179053.

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Latifi, Seyed Amir, Hassan Ghassemian, and Maryam Imani. "Feature Extraction and Classification of Respiratory Sound and Lung Diseases." In 2023 6th International Conference on Pattern Recognition and Image Analysis (IPRIA). IEEE, 2023. http://dx.doi.org/10.1109/ipria59240.2023.10147191.

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Tuba, Eva, Ivana Strumberger, Nebojsa Bacanin, and Milan Tuba. "Analysis of local binary pattern for emphysema classification in lung CT image." In 2019 11th International Conference on Electronics, Computers and Artificial Intelligence (ECAI). IEEE, 2019. http://dx.doi.org/10.1109/ecai46879.2019.9042056.

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Nasrullah, Nasrullah, Jun Sang, Mohammad S. Alam, and Hong Xiang. "Automated detection and classification for early stage lung cancer on CT images using deep learning." In Pattern Recognition and Tracking XXX, edited by Mohammad S. Alam. SPIE, 2019. http://dx.doi.org/10.1117/12.2520333.

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Shea, Daniel E., Sourabh Kulhare, Rachel Millin, Zohreh Laverriere, Courosh Mehanian, Charles B. Delahunt, Dipayan Banik, et al. "Deep Learning Video Classification of Lung Ultrasound Features Associated with Pneumonia." In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2023. http://dx.doi.org/10.1109/cvprw59228.2023.00312.

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Dandil, Emre, Murat Cakiroglu, Ziya Eksi, Murat Ozkan, Ozlem Kar Kurt, and Arzu Canan. "Artificial neural network-based classification system for lung nodules on computed tomography scans." In 2014 6th International Conference of Soft Computing and Pattern Recognition (SoCPaR). IEEE, 2014. http://dx.doi.org/10.1109/socpar.2014.7008037.

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Ming, Joel Than Chia, Norliza Mohd Noor, Omar Mohd Rijal, Rosminah M. Kassim, and Ashari Yunus. "Lung disease severity classification using reticular pattern and five class features based on GLCM." In TENCON 2017 - 2017 IEEE Region 10 Conference. IEEE, 2017. http://dx.doi.org/10.1109/tencon.2017.8227860.

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Gan, Bin, Chun-Hou Zheng, and Hong-Qiang Wang. "A survey of pattern classification-based methods for predicting survival time of lung cancer patients." In 2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2014. http://dx.doi.org/10.1109/bibm.2014.6999296.

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