A Convolutional Neural Network Classifies Intestinal Diseases Using Endoscopic Images

Abstract

Intestinal diseases such as polyps, esophagitis, and ulcerative colitis may lead to serious complications when they are not detected early. Endoscopy provides important visual information for diagnosis; however, manual interpretation still depends on clinical expertise and requires time. This study aimed to develop an automatic intestinal disease classification model based on Convolutional Neural Network (CNN). The study used a Research and Development method consisting of data collection, preprocessing, model training, evaluation, and web interface implementation. The dataset was obtained from Kaggle and consisted of 6,000 endoscopic images categorized into normal, polyps, esophagitis, and ulcerative colitis. Each class contained 1,500 images, divided into approximately 87% training data and 13% testing data. The model was trained for 15 epochs and evaluated using accuracy, loss, confusion matrix, and single-image testing. The results showed stable validation accuracy in the range of 98-99%, while single-image testing produced confidence scores from 99.91% to 100%. This system is recommended as an initial endoscopic image classification aid, with further development involving a random-image class and an examination history database.