Abstract:
Gastrointestinal diseases refer to diseases of the gastro intestinal tract and include; gastro
esophagitis, ulcerative colitis, gastric lesions among others. These diseases are best
diagnosed at an early stage, and therefore, the need for enhanced diagnostic methods that
allow for same. This paper proposes the use of Convolutional Neural Networks (CNNs) in
discerning gastrointestinal diseases from endoscopy images in an effort to make diagnosis
more accurate as well as faster. The research assesses different CNN architectures –
DenseNet201 and InceptionResNetV2, VGG19, and the CNN models: CNN01 and CNN02
developed by the authors specifically for this research investigation. A comprehensive set
of images involving gastrointestinal diseases such as esophagitis, ulcerative colitis, dyed
resection margins, normal pylorus were used for training and testing. The experimental
findings also reveal that the built own CNN01 model yielded the highest average accuracy
of 98.56%. These research outcomes show the effectiveness of the high-architectural CNN
in achieving good classification of gastrointestinal diseases from endoscopic pictures. In
light of the study of CNN, potential future applications of CNN are presented to
demonstrate how CNN could improve diagnostic accuracy within gastroenterology and
reduce the necessity of operation. The future of this kind of research will involve handling
of a diverse dataset, working to reduce the class imbalance issue, and enhance the explain
ability of the model.
