AI-Driven Colon Disease Diagnosis Comprehensive MobileNetV2 Analysis with Biomarkers and Advanced Imaging

Colon diseases cover a wide and intricate range of ailments that are difficult to detect and treat. Traditional diagnostic approaches, sometimes intrusive and dependent on subjective evaluations, might produce inconsistent outcomes and cause patient discomfort. Colonoscopy, Endoscopy, CT scan (computed tomography) and MRI (magnetic resonance imaging) are recent imaging technology advancements that have significantly improved diagnostic precision and early detection. This study investigates the most recent advances in non-invasive diagnostic procedures, with a special emphasis on the incorporation of artificial intelligence (AI) and biomarker methodologies. AI and deep learning (DL) models can evaluate massive volumes of medical data, such as imaging results and medical data, which show nuances and connections that human research might overlook. This study classified a wide collection of four essential categories, including normal, polyps, esophagitis, and ulcerative diseases, using an artificial intelligence-powered tool (MobilenetV2) to improve accuracy and precision that is early detection and encourage specific treatment methods. This system achieved 99.19% training accuracy, 93.55% validation, and 94.25% testing accuracy. By reducing the reliance on invasive procedures, these innovations hold the potential to revolutionize colon disease management and improve patient outcomes.