A hybrid XAI-driven deep learning framework for robust GI tract disease diagnosis

The stomach is one of the main digestive organs in the GIT, essential for digestion and nutrient absorption. However, various gastrointestinal diseases, including gastritis, ulcers, and cancer, affect health and quality of life severely. The precise diagnosis of gastrointestinal (GI) tract diseases is a significant challenge in the field of healthcare, as misclassification leads to late prescriptions and negative consequences for patients. Even with the advancement in machine learning and explainable AI for medical image analysis, existing methods tend to have high false negative rates which compromise critical disease cases. This paper presents a hybrid deep learning based explainable artificial intelligence (XAI) approach to improve the accuracy of gastrointestinal disorder diagnosis, including stomach diseases, from images acquired endoscopically. Swin Transformer with DCNN (EfficientNet-B3, ResNet-50) is integrated to improve both the accuracy of diagnostics and the interpretability of the model to extract robust features. Stacked machine learning classifiers with meta-loss and XAI techniques (Grad-CAM) are combined to minimize false negatives, which helps in early and accurate medical diagnoses in GI tract disease evaluation. The proposed model successfully achieved an accuracy of 93.79% with a lower misclassification rate, which is effective for gastrointestinal tract disease classification. Class-wise performance metrics, such as precision, recall, and F1-score, show considerable improvements with false-negative rates being reduced. AI-driven GI tract disease diagnosis becomes more accessible for medical professionals through Grad-CAM because it provides visual explanations about model predictions. This study makes the prospect of using a synergistic DL with XAI open for improvement towards early diagnosis with fewer human errors and also guiding doctors handling gastrointestinal diseases.