Adenocarcinoma recognition in endoscopy images using optimized convolutional neural networks

Abstract

Colonoscopy, which refers to the endoscopic examination of colon using a camera, is considered as the most effective method for diagnosis of colorectal cancer. Colonoscopy is performed by a medical doctor who visually inspects one's colon to find protruding or cancerous polyps. In some situations, these polyps are difficult to find by the human eye, which may lead to a misdiagnosis. In recent years, deep learning has revolutionized the field of computer vision due to its exemplary performance. This study proposes a Convolutional Neural Network (CNN) architecture for classifying colonoscopy images as normal, adenomatous polyps, and adenocarcinoma. The main objective of this study is to aid medical practitioners in the correct diagnosis of colorectal cancer. Our proposed CNN architecture consists of 43 convolutional layers and one fully-connected layer. We trained and evaluated our proposed network architecture on the colonoscopy image dataset with 410 test subjects provided by Gachon University Hospital. Our experimental results showed an accuracy of 94.39% over 410 test subjects. © 2020 by the authors.