MNETGIDD: A heuristic-oriented segmentation and deep learning multi-disease detection model for gastrointestinal tracts

Abstract

Malignant growth of the gastrointestinal tract is among the leading causes of death worldwide. Research indicates that almost 40% of people worldwide suffer from long-term digestive issues. According to a study published in the United European Gastroenterology Journal, the occurrence of a digestive disorders has increased since 2000. Digestive disorders continue to be a major cause of death even with a slight decline. The WHO MORTALITY DATABASE reported huge death rates in every year due to the GI Diseases. From that report, the need of an accurate detection of GI Tract malignant in low cost and error prone labor must be developed. This work introduces MNET Gastro Intestinal (GI) Disease Detection (MNETGIDD), which is a complete identification model for multi-gastrointestinal disease discovery from clinical images. MNETGIDD model Using Gastrolab dataset with endoscopic images and it works as pipelines that are pre-processed, segmented and identify the affected areas.  This proposed approach aims to enhance image quality, facilitate accurate segmentation and classification, the entire process through a pipeline process, initially preprocessing with techniques such as text removal, illumination enhancement, and fuzzy histogram equalization. During segmentation, Otsu Segmentation based on Krill-Herd Optimization is used to identify the affected area. The MNETGIDD model incorporates the MobileNetV2 architecture, designed for light weight classification model working under resource-constrained environments. According to the tests, the MNETGIDD model exhibits high sensitivity and specificity, outperforming human experts in many cases. In terms of accuracy, the model achieved 96.349%, a precision 96.25 %, and a recall of 97.08%. This deep learning system has the potential to revolutionize gastrointestinal disease diagnostics and screening by automating key steps and improving patient outcomes.