Deep Learning Approach for Diagnosis of Skin Cancer Using Dermascopic Images

ABSTRACT Skin cancer, a potentially life-threatening condition, necessitates early and accurate diagnosis to improve survival rates. This paper presents an automated and reliable deep learning solution for classifying skin lesions as malignant or benign using dermoscopic images. Leveraging DenseNet-201, a powerful pre-trained convolutional neural network, the system utilizes transfer learning to extract high-level, disease-specific features from dermoscopic data. This approach addresses limitations in earlier methods by enhancing feature representation and classification performance. To further boost accuracy and reduce overfitting, the model incorporates data augmentation techniques such as rotation, flipping, and zooming, along with systematic hyperparameter tuning. The final model demonstrates improved accuracy of 88% and generalization compared to prior benchmarks, while maintaining computational efficiency and scalability. Designed for integration into clinical workflows, this solution empowers healthcare professionals with a precise, Deep Learning based diagnostic tool for early detection and treatment planning of skin cancer. Keywords: Skin Cancer Detection, Deep Learning, DenseNet-201, Transfer Learning, Data Augmentation, Classification Accuracy.