EffiDerm: An Efficient Deep Learning Model for Skin Cancer Prediction

Skin cancer is one of the most prevalent cancers worldwide, and early detection significantly improves patient outcomes. This paper introduces EffiDerm, an Efficient Convolutional Neural Network (CNN) that can accurately classify seven different kinds of skin lesions. To solve dataset imbalance and improve generalization, the model uses sophisticated preprocessing techniques like adaptive class balancing and data augmentation. The lightweight model outperforms bigger conventional architectures while maintaining computing efficiency and being mobile deployment-friendly, achieving a classification accuracy of 93.07% with only 1.4 million parameters. It is especially helpful in healthcare settings with limited resources because of its real-time inference capability. Expanding access to skin cancer screening, lowering the chance of misclassification, and helping dermatologists with early detection are all possible outcomes of the suggested system. Future research will concentrate on verifying the model in actual clinical settings and including explainable AI for enhanced interpretability.