Early detection of Alzheimer's disease (AD) is vital for effective treatment, as interventions are most successful in the disease's early stages. Combining Magnetic Resonance Imaging (MRI) with artificial intelligence (AI) offers significant potential for enhancing AD diagnosis. However, traditional AI models often lack transparency in their decision-making processes. Explainable Artificial Intelligence (XAI) is an evolving field that aims to make AI decisions understandable to humans, providing transparency and insight into AI systems. This research introduces the Squeeze-and-Excitation Convolutional Neural Network with Random Forest (SECNN-RF) framework for early AD detection using MRI scans. The SECNN-RF integrates Squeeze-and-Excitation (SE) blocks into a Convolutional Neural Network (CNN) to focus on crucial features and uses Dropout layers to prevent overfitting. It then employs a Random Forest classifier to accurately categorize the extracted features. The SECNN-RF demonstrates high accuracy (99.89%) and offers an explainable analysis, enhancing the model's interpretability. Further exploration of the SECNN framework involved substituting the Random Forest classifier with other machine learning algorithms like Decision Tree, XGBoost, Support Vector Machine, and Gradient Boosting. While all these classifiers improved model performance, Random Forest achieved the highest accuracy, followed closely by XGBoost, Gradient Boosting, Support Vector Machine, and Decision Tree which achieved lower accuracy.

Accurate diagnosis of brain tumors from MRI scans is crucial for effective treatment planning and patient outcomes. Deep learning techniques have shown promise in automating this process, but the interpretability of their decisions remains a challenge. This paper introduces the Squeeze-and-Excitation Convolutional Neural Network-MobileNet (SECNN-MNet), a novel deep learning model designed for precise and interpretable classification of brain tumors in MRI images. SECNN-MNet combines the robust feature extraction capabilities of SE-CNN with the computational efficiency of MobileNet. By integrating SE blocks, the model dynamically recalibrates feature maps to capture crucial tumor characteristics, enhancing diagnostic accuracy. MobileNet’s depthwise separable convolutions optimize computational resources while preserving image detail fidelity. Our model achieves exceptional performance, demonstrating an accuracy of 99.6% in classifying brain tumors. Leveraging explainable artificial intelligence (XAI), SECNN-MNet provides insights into decision-making processes, enhancing its interpretability and utility for clinicians. This study underscores the potential of SECNN-MNet in advancing automated brain tumor diagnosis, offering both high accuracy and interpretability crucial for clinical adoption and patient care.

Accurate early prediction and multi-stage diagnostic classification compliant with AJCC TNM staging criteria are essential for effective treatment planning in pancreatic cancer. Although deep learning (DL) approaches show promising results, interpreting their predictions remains challenging. This study proposes Squeeze-and-Excitation Visual-Efficient Tabular Network (SE-VETNet), an interpretable multimodal DL framework designed for early prediction and multi-stage pancreatic cancer diagnostic classification using medical imaging and clinical data. The framework integrates Visual Geometry Group (VGG) and EfficientNet architectures enhanced with Squeeze-and-Excitation (SE) blocks for feature recalibration, alongside Tabular Network (TabNet) for clinical feature extraction. Attention mechanisms identify relevant tumor characteristics and clinical attributes. An optimized processing pipeline preserves image details and clinical data integrity while improving computational efficiency. To address class imbalance, balancing is applied exclusively to training data, while five-fold cross-validation supports robust evaluation and minimizes potential bias. Experiments demonstrate consistent performance across all folds, achieving a mean test accuracy of 99.21% with minimal variability across five diagnostic cohorts (Healthy and T1–T4). Furthermore, Explainable Artificial Intelligence (XAI) techniques, including Gradient-weighted Class Activation Mapping (Grad-CAM), quantitatively validated against expert ground-truth masks via Intersection over Union (IoU), and TabNet attention, provide insights into image-based and clinical feature contributions, respectively. By integrating multimodal learning, robust evaluation, and explainability, SE-VETNet offers a promising approach to early pancreatic cancer prediction, multi-stage staging, and transparent clinical decision-making. However, validation using independent and diverse clinical datasets remains necessary to establish generalizability and assess its suitability for practical deployment.