An Improved ResNet-Based Deep Learning Model with Modified Mountain Gazelle Optimization for Gastrointestinal Disease Classification

Abstract

Gastrointestinal (GI) tract diseases occur due to abnormalities affecting different regions of the digestive system. Diagnosing GI tract diseases typically involves methods such as endoscopy, imaging examinations, biopsy analysis, and clinical assessments. The severity and stage of the disease play a crucial role in determining the appropriate treatment plan. Medical imaging techniques are used to identify abnormal regions and assess the progression of diseases. The categorization of GI tract diseases, including ulcerative lesions, polyps, bleeding regions, inflammatory conditions, and normal tissues, is crucial for accurate diagnosis and effective treatment planning. Recently, Artificial Intelligence (AI)-based Deep Learning (DL) models have received greater attention due to their accuracy and flexibility. In this work, a threefold DL model is suggested to classify GI tract disease types. An improved ResNet, called Channel-Spatial Context Gating ResNet (CSCG-ResNet), is proposed for efficient feature extraction. Then, a modified Mountain Gazelle Optimizer (MGO) is proposed for feature optimization. Finally, the Quantum-inspired TabNet is proposed for multi-class categorization. The performance of the model is validated using the Kvasir dataset. The model achieves an overall accuracy of 96.75% when compared to previously proposed models.