S. Rajagopal1,*, T. Ramakrishnan2, S. Vairaprakash3
Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 1927-1941, 2022, DOI:10.32604/iasc.2022.024998
Abstract Video capsule endoscope (VCE) is a developing methodology, which permits analysis of the full gastrointestinal (GI) tract with minimum intrusion. Although VCE permits for profound analysis, evaluating and analyzing for long hours of images is tiresome and cost-inefficient. To achieve automatic VCE-dependent GI disease detection, identifying the anatomical region shall permit for a more concentrated examination and abnormality identification in each area of the GI tract. Hence we proposed a hybrid (Long-short term memory-Visual Geometry Group network) LSTM-VGGNET based classification for the identification of the anatomical area inside the gastrointestinal tract caught by VCE images. The video input data is… More >
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