Lung cancer is a serious global threat that demands immediate attention for early detection and accurate diagnosis. This paper presents a new multistage deep learning framework that uses pre-trained convolutional neural networks such as EfficientNetB0, VGG16, MobileNetV2, DenseNet121, and ResNet50. It also introduces a hybrid CNN-SVM model to improve classification success. This approach combines adaptive loss optimization, anomaly detection, and explainable AI with Grad-CAM and t-SNE. It ensures high decision accuracy and strong reliability. In our current methods, it achieves an impressive 99.55 percent test accuracy on a specialized lung radiograph dataset. This innovation combines interpretability, strength, and diagnostic capability, marking a significant advance for AI in medicine. It paves the way for smarter systems that help doctors predict lung cancer more confidently and accurately.

Skin cancer is one of the most life-threatening diseases in humans, but early and accurate detection can drastically improve patient outcomes. In this work, we present a comprehensive benchmarking and optimization study of stateof-the-art deep learning models for dermatoscopic skin lesions classification. We evaluated multiple CNN architectures, including VGG variants (VGG11, VGG13, VGG16, VGG19), ResNet18, ResNet34, MobileNetV2, DenseNet121, and EfficientNetB0. To push performance boundaries, we integrate cutting-edge optimization techniques such as RAdam, Lookahead, and Ranger, along with advanced architectures like Vision Transformers (ViT with Folds), DenseNet with AlphaTensor-enhanced training, and Self-Distilling ConvNeXt combined with 5-Fold Ensembling. Our experiments demonstrate that a fine-tuned DenseNet model with hybrid optimizers and AlphaTensor-driven enhancements achieves a state-of-the-art validation accuracy of 91.67 percent, significantly outperforming conventional pipelines. This study provides a comprehensive comparative framework and highlights novel strategies for building next-generation AI-driven diagnostic systems for the detection of skin cancer.