An ensemble approach of deep CNN models with Beta normalization aggregation for gastrointestinal disease detection

Gastrointestinal (GI) diseases present a significant healthcare challenge, requiring the development of accurate and efficient diagnostic methods. Traditional diagnostic methods often use single models, which have difficulty capturing the complex and varied patterns of these conditions. To address this limitation, we propose a groundbreaking ensemble method specifically designed for the detection of GI. Our approach strategically selects three robust base models-EfficientNetB0, EfficientNetB2, and ResNet101-leveraging transfer learning to utilize their pre-trained weights and feature representations. This foundation accelerates training and enhanced the models' capacity to discern complex patterns associated with gastrointestinal conditions. Our methodology is centered around a novel beta normalization aggregation scheme that combines insights from individual models according to their confidence scores. This refined aggregation approach culminates in a nuanced ensemble model that improves overall predictive accuracy. We rigorously evaluate our proposed method on two established gastrointestinal datasets-one comprising four classes and the other three classes-achieving exceptional accuracies of 97.88% and 97.47%, respectively. Notably, our approach not only outperforms the individual base models but also surpasses existing methodologies in gastrointestinal diagnosis. Using Grad-CAM analysis, we present visualizations of the decision-making processes in our models, which enhanced both interpretability and trustworthiness. Unlike conventional ensemble methods that utilize basic summation or other traditional strategies, our innovative weighted average strategy and improved beta normalization scheme position our ensemble method as a powerful and reliable tool for accurate gastrointestinal disease detection. This advancement holds the potential to significantly enhance diagnostic precision in the complex landscape of gastrointestinal health, offering new avenues for clinical application and improved patient outcomes.