DIAGNOSIS AND ANALYSIS OF HYPER-TUNING DEEP LEARNING MODEL FOR AUTOMATIC DIAGNOSING CELIAC DISEASE

Celiac disease is an immune system situation that mostly impacts and damages the small intestine as well as the skeletal system. Celiac Disease is prevalent in the modern population. Individuals with Celiac disease are unable to ingest gluten without experiencing negative health consequences. Insufficient awareness often leads to delayed disease identification. Utilizing computer-based prediction could aid in the early identification of Celiac Disease in individuals, increasing their likelihood of maintaining a typical life. The deep learning approach is conducted using hyper-tuning. The hyperparameters of the Mobile Net classifier are optimized using a novel hybrid approach that combines Wheel Plant Optimization and Fruit Fly Optimization. The suggested model is finally assessed and compared with existing approaches regarding Accuracy, Precision, Recall, and Time. The proposed model demonstrated superior performance by attaining an Accuracy of 99.78% compared to the other methods.