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Analysing TB Severity Levels with an Enhanced Deep re-sidual Learning - Depth-Resnet.

, , and . CLEF (Working Notes), volume 2125 of CEUR Workshop Proceedings, CEUR-WS.org, (2018)

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The Application of KAZE Features to the Classification Echocardiogram Videos., , , and . MRDM@ECIR, volume 9059 of Lecture Notes in Computer Science, page 61-72. Springer, (2015)Deep learning for detection and segmentation of artefact and disease instances in gastrointestinal endoscopy., , , , , , , , , and 27 other author(s). Medical Image Anal., (2021)Analysis of tuberculosis severity levels from CT pulmonary images based on enhanced residual deep learning architecture., , and . Neurocomputing, (2020)A collection of benchmark images for traffic sign research.. DSP, page 1-6. IEEE, (2011)Artefact Detection and Segmentation based on a Deep Learning system., and . EndoCV@ISBI, volume 2595 of CEUR Workshop Proceedings, page 80-81. CEUR-WS.org, (2020)An Evolutionary Approach to Automated Class-Specific Data Augmentation for Image Classification., , , and . DIS, volume 14321 of Lecture Notes in Computer Science, page 170-185. Springer, (2023)Vision Models Based Identification of Traffic Signs., , , , , , , and . CGIV, page 47-51. IS&T - The Society for Imaging Science and Technology, (2002)Detection of human papillomavirus (HPV) from super resolution microscopic images applying a texture transformer network., , , , , and . Medical Imaging: Biomedical Applications in Molecular, Structural, and Functional Imaging, volume 12036 of SPIE Proceedings, SPIE, (2022)From medical imaging to medical informatics., , and . Comput. Methods Programs Biomed., 92 (3): 225-226 (2008)Recognition of traffic signs based on their colour and shape features extracted using human vision models., , , , and . J. Vis. Commun. Image Represent., 17 (4): 675-685 (2006)