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

Affiliation

Mohapatra S(1), Nayak J(2), Mishra M(3), Pati GK(4), Naik B(5), Swarnkar T(6).
Author information:
(1)Department of Computer Science and Engineering, Siksha O Anusandhan
(Deemed to Be University), Bhubaneswar, 751030, India.
(2)Department of Computer Science, Aditya Institute of Technology and Management, Srikakulam, Andhra Pradesh, 532201, India. [Email]
(3)Department of Electrical and Electronics Engineering, Siksha O Anusandhan Deemed to Be University, Bhubaneswar, 751030, India.
(4)Department of Gastroenterology, Institute of Medical Science, SUM Hospital, Bhubaneswar, 751030, India.
(5)Department of Computer Application, Veer Surendra Sai University of Technology, Burla, 768018, India.
(6)Department of Computer Application, Siksha O Anusandhan
(Deemed to Be University), Bhubaneswar, 751030, India.

Abstract

This 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 training 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 for 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, and F1 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.