Brain tumors are among the most severe health challenges, necessitating early and precise diagnosis for effective treatment planning. This study introduces an optimized hybrid transfer learning (TL) framework for brain tumor classification using magnetic resonance imaging images. The proposed system integrates advanced preprocessing techniques, an ensemble of pretrained deep learning models, and explainable artificial intelligence (XAI) methods to achieve high accuracy and reliability. The methodology enhances image quality through noise reduction and contrast enhancement, facilitating robust feature extraction. The ensemble model combines VGG16 and ResNet152V2 architectures, achieving a classification accuracy of 99.47% on a challenging four‐class dataset. Additionally, gradient‐weighted class activation mapping and SHapley Additive exPlanations (SHAP)‐based XAI techniques provide visual and quantitative insights into model predictions, improving interpretability and clinical trust. This comprehensive framework demonstrates the potential of hybrid TL and XAI in advancing diagnostic accuracy and supporting clinical decision‐making for brain tumor detection. The results underscore its applicability in clinical settings, particularly in resource‐constrained environments.

Global attention is now being paid to maternal and child mortality. The incidence of maternal mortality is high in low and middle-income countries, particularly among adolescents and young adults. Healthcare professionals can monitor the mother's heartbeat during pregnancy to determine fetal viability using CTGs to prevent these deaths. To reduce child and maternal mortality, this work presented a risk factor analysis using machine learning approaches. As part of this study, this work evaluated seven machine learning algorithms. To assess the performance of different categorization algorithms, accuracy, precision, and recall were used. The random forest has achieved the highest 99.98% accuracy among the other algorithms. Initially, the dataset was imbalanced, after applying undersampling and oversampling methods, all algorithms performed excellently. A major focus of the present study was to predict the risk factor of child and maternal mortality using clinical data. Sending an ultrasound pulse and reading the response is how ultrasound devices work. To prevent child and maternal mortality, this analysis is an effective and cost-effective option for healthcare professionals.

X-ray imaging remains a cornerstone in medical diagnostics for conditions such as bone fractures, knee osteoarthritis, and lung diseases. However, variability in image quality and dataset diversity presents significant challenges for automated analysis using deep learning models. This study addresses these issues by proposing an EfficientNet B0 architecture enhanced with a Convolutional Block Attention Module (CBAM) to improve classification accuracy and interpretability across multiple X-ray datasets: FracAtlas, Knee, and Lung X-ray. A robust preprocessing pipeline comprising LAB color space conversion, morphological filtering, gamma correction, Non-Local Means denoising, resizing, and normalization was applied to optimize image quality, with each step’s effectiveness verified through established image quality metrics. Additionally, geometric augmentation techniques were performed to increase dataset variability and improve model generalization. Comparative experiments with transfer learning, transformer-based, and attention-based models identified the attention-based EfficientNet B0 as the best performer. An extensive ablation study on the Knee X-ray dataset refined hyperparameters to maximize performance. The optimized model achieved high test accuracies of 98.09%, 97.12%, and 99.51% on FracAtlas, Knee, and Lung datasets, respectively. Further analyses, including noise robustness testing, k-fold cross-validation, and Grad-CAM visualization, demonstrated the model’s resilience, consistency, and explainability. The findings highlight the critical role of attention mechanisms in enhancing feature representation and generalization across heterogeneous medical imaging tasks. This work lays a foundation for reliable, interpretable AI systems that can support clinical decision-making, with future efforts focusing on expanding dataset diversity and real-world clinical validation to accelerate adoption.