<scp>StackDeVNet</scp> : An Explainable Stacking Ensemble of <scp>DenseNets</scp> and Vision Transformers for Advanced Gastrointestinal Disease Detection

ABSTRACT Gastrointestinal disorders include diseases that negatively affect people's daily life and carry the risk of cancer. Therefore, accurate and early diagnosis of these diseases is important for treatment process of patients. Deep learning architectures, which have achieved significant success in medical image analysis, are effectively used in early diagnosis and diagnosis systems. Therefore, in this study, a new approach that achieves higher accuracy in the detection of gastrointestinal diseases by combining DenseNet and Vision Transformer models with stacking ensemble is proposed. As a result of the experiments, the proposed model achieved 99.06% accuracy in a single test and 98.64% accuracy on mean as a result of 5‐fold cross‐validation. The proposed approach shows promising accuracy and reliability as evidenced by the results of experiments on the KvasirV2 dataset, and has the potential to be an effective method for the detection of gastrointestinal diseases. To improve model interpretability, the Explainable AI technique Grad‐ CAM and attention map visualizations were used, allowing visual justification of the model's predictions and highlighting clinically relevant regions in endoscopic images. The model obtained by combining DenseNet and Vision Transformer models with the stacking ensemble method is expected to be an example for future studies in the field of health and image processing, especially gastrointestinal diseases.