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

Abstract

Malignant growth of the gastrointestinal (GI) 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, digestive disorders have increased since 2000. Digestive disorders continue to be a major cause of death, even with a slight decline. The World Health Organization’s Mortality Database reported huge death rates every year due to GI diseases. From that report, the need to accurately detect GI tract malignant in low-cost and error-prone labor must be developed. This work introduces MNET Gastrointestinal Disease Detection (MNETGIDD), which is a complete identification model for multi-gastrointestinal disease discovery from clinical images. MNETGIDD model uses the Gastrolab dataset with endoscopic images, acting as pipelines that are pre-processed and segmented to identify the affected areas. This proposed approach aims to enhance image quality and facilitate accurate segmentation and classification 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 was used to identify the affected area. The MNETGIDD model incorporates the MobileNetV2 architecture, designed for a lightweight classification model working under resource-constrained environments. According to the tests, the MNETGIDD model exhibits high sensitivity and specificity, often outperforming human experts. In terms of accuracy, the model achieved 96.349%, a precision of 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.

Keywords

Fuzzy Histogram Equalization / Gastrointestinal Disease Detection / MNETGIDD / Gastrolab dataset / Lowlight Image Enhancement / Mean-Shift Segmentation / MobileNetV2

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