An optimized ensemble model bfased on cuckoo search with Levy Flight for automated gastrointestinal disease detection

Accurate detection of gastrointestinal (GI) diseases is critical for effective medical intervention. Existing methods often lack accuracy and efficiency, emphasizing the need for more advanced approaches. The complexity and diversity of medical image data, such as those found in GI diseases, can pose challenges for a single model to comprehensively represent all essential features. In such scenarios, an ensemble learning approach becomes important. In this paper, we propose an innovative ensemble learning approach for GI disease prediction. We leverage the power of three transfer learning models, DenseNet169, InceptionV3, and MobileNet, as base learners along with additional layers to effectively learn data-specific features. We implement a weighted averaging ensemble strategy to merge predictions from individual base models and fine-tune the weights using the cuckoo search (CS) with levy flight algorithm. This approach results in more accurate predictions compared to individual models, leveraging the diverse strengths of the base learners for enhanced performance in GI disease prediction. This study is notably the pioneer in introducing a metaheuristics-based optimized model for the detection of GI diseases. We assess the presented model using a publicly accessible endoscopic image dataset that consists of 6,000 images. The results demonstrate exceptional predictive accuracy, with the ensemble achieving an outstanding accuracy of 99.75%. Through Grad-CAM analysis, we gain valuable insights into the decision-making process of the individual base models, enabling us to identify areas of strength and improvement. Our proposed ensemble model outperforms traditional weight assignment methods and existing state-of-the-art methods, showcasing its superiority in GI disease prediction. Our approach has transformative potential in medical image analysis, promising enhanced patient care and diagnostic accuracy in gastroenterology.