New Deep Learning Approaches for Binary Skin Cancer Classification

Skin cancer remains one of the most prevalent malignancies worldwide, with melanoma accounting for the most lethal form due to its high metastatic potential. Early and accurate diagnosis is essential to improving patient survival, yet access to specialized dermatological expertise is limited in many regions. Recent advances in deep learning, particularly Convolutional Neural Networks (CNNs), have significantly enhanced the capabilities of computer-aided diagnosis (CAD) systems. This study introduces and evaluates three lightweight and optimized CNN-based architectures for binary skin cancer classification: (1) a Modified MobileNet with Residual Blocks, (2) an AlexNet enhanced with Squeeze-and-Excitation (SE) Attention, and (3) a custom-designed CNN with integrated Residual Connections. Using a benchmark dermoscopic dataset from the ISIC Archive, we apply standardized preprocessing and data augmentation techniques, followed by rigorous model training and evaluation. Results show that the Modified CNN achieves the highest accuracy (84.70%), precision (84.56%), recall (84.78%), and F1-score (84.63%), outperforming or matching state-of-the-art models such as ResNet-101, while maintaining computational efficiency. These findings support the feasibility of deploying such models in mobile health applications, offering a scalable solution for early melanoma screening in resource-constrained environments.