Skin cancer is among the most rapidly increasing forms of malignancy worldwide, requiring early and accurate detection for effective treatment and survival improvement. Traditional diagnostic approaches rely heavily on dermatological expertise and visual examination, which are often subjective and time-consuming. Recent advancements in deep learning, particularly convolutional neural networks (CNNs), have demonstrated significant potential in automating skin lesion classification with high accuracy and consistency. This study proposes an optimized CNN-based architecture designed for enhanced feature extraction, improved generalization, and robust classification of dermoscopic skin lesion images.

The proposed system integrates advanced convolutional blocks inspired by modern lightweight architectures and deep feature fusion strategies to improve performance across heterogeneous datasets. The methodology emphasizes preprocessing, data augmentation, optimized feature learning, and classification refinement. The study also evaluates the system in the context of established dermatological research and machine learning models, highlighting its superiority in diagnostic accuracy and computational efficiency.

Existing literature confirms the effectiveness of deep learning-based models in skin cancer detection; however, challenges such as overfitting, class imbalance, and limited interpretability remain critical barriers. By addressing these issues, the proposed model contributes to improved clinical decision support systems. The findings suggest that optimized CNN architectures can significantly enhance early detection capabilities and reduce diagnostic uncertainty in dermatology.