Wavelet Transform and Deep Convolutional Neural Network-Based Smart Healthcare System for Gastrointestinal Disease Detection

AbstractThis work presents a smart healthcare system for the detection of various abnormalities present in the gastrointestinal (GI) region with the help of time –frequency analysis and convolutional neural network. In this regard, the KVASIR V2 dataset comprising of eight classes of GI-tract images such as Normal cecum, Normal pylorus, Normal Z-line, Esophagitis, Polyps, Ulcerative Colitis, Dyed and lifted polyp, and Dyed resection margins are used for tr aining and validation. The initial phase of the work involves an image pre-processing step, followed by the extraction of approximate discrete wavelet transform coefficients. Each class of decomposed images is later given as input to a couple of considered convolutional neural network (CNN) models f or training and testing in two different classification levels to recognize its predicted value. Afterward, the classification performance is measured through the following measuring indices: accuracy, precision, recall, specificity, andF1 score. The experimental result shows 97.25% and 93.75% of accuracy in the first level and second level of classification, respectively. Lastly, a comparative performance analysis is carried out with several other previously published works on a similar dataset where the proposed approach performs better than its contemporary methods.Graphic Abstract

Source: Interdisciplinary Sciences, Computational Life Sciences - February 10, 2021 Category: Bioinformatics Source Type: research