GastroViT: A Vision Transformer Based Ensemble Learning Approach for Gastrointestinal Disease Classification with Grad CAM & SHAP Visualization

The gastrointestinal (GI) tract of humans can have a wide variety of aberrant mucosal abnormality findings, ranging from mild irritations to extremely fatal illnesses. Prompt identification of gastrointestinal disorders greatly contributes to arresting the progression of the illness and improving therapeutic outcomes. This paper presents an ensemble of pre-trained vision transformers (ViTs) for accurately classifying endoscopic images of the GI tract to categorize gastrointestinal problems and illnesses. ViTs, attention-based neural networks, have revolutionized image recognition by leveraging the transformative power of the transformer architecture, achieving state-of-the-art (SOTA) performance across various visual tasks. The proposed model was evaluated on the publicly available HyperKvasir dataset with 10,662 images of 23 different GI diseases for the purpose of identifying GI tract diseases. An ensemble method is proposed utilizing the predictions of two pre-trained models, MobileViT_XS and MobileViT_V2_200, which achieved accuracies of 90.57% and 90.48%, respectively. All the individual models are outperformed by the ensemble model, GastroViT, with an average precision, recall, F1 score, and accuracy of 69%, 63%, 64%, and 91.98%, respectively, in the first testing that involves 23 classes. The model comprises only 20 million (M) parameters, even without data augmentation and despite the highly imbalanced dataset. For the second testing with 16 classes, the scores are even higher, with average precision, recall, F1 score, and accuracy of 87%, 86%, 87%, and 92.70%, respectively. Additionally, the incorporation of explainable AI (XAI) methods such as Grad-CAM (Gradient Weighted Class Activation Mapping) and SHAP (Shapley Additive Explanations) enhances model interpretability, providing valuable insights for reliable GI diagnosis in real-world settings.

CBAM Integrated Attention Driven Model For Betel Leaf Diseases Classification With Explainable AI

Betel leaf is an important crop because of its economic advantages and widespread use. Its betel vines are susceptible to a number of illnesses that are commonly referred to as betel leaf disease. Plant diseases are the largest threat to the food supply's security, and they are challenging to identify in time to stop possible financial damage. Interestingly, artificial intelligence can leave a big mark on the betel leaf industry since it helps with output growth by forecasting sickness. This paper presents a lightweight CBAM-CNN model with just 2.13 million parameters (8.13 MB), incorporating CBAM (Convolutional Block Attention Module) to improve feature emphasis without depending on heavy pre-trained networks. The model's capacity to discern minute variations among leaf disease classes is improved by the integrated attention mechanism, which allows it to adaptively focus on significant spatial and channel-wise information. In order to ensure class balance and diversity for efficient model training and validation, this work makes use of an enriched dataset of 10,185 images divided into three categories: Healthy Leaf, Leaf Rot, and Leaf Spot. The proposed model achieved a precision of 97%, recall of 94%, and F1 score of 95%, and 95.58% accuracy on the test set demonstrating strong and balanced classification performance outperforming traditional pre trained CNN models. The model's focus regions were visualized and interpreted using Grad-CAM (Gradient-weighted Class Activation Mapping), an explainable AI technique.