Skin cancer detection using deep learning method

Skin cancer remains one of the most common and potentially fatal forms of cancer globally, underscoring the critical need for early and accurate diagnosis. This paper presents a novel deep learning framework designed to automatically detect skin cancer from dermatoscopic images. Utilizing advanced convolutional neural network (CNN) architectures, the proposed model is capable of learning discriminative features directly from raw image data, thereby reducing the reliance on manual feature extraction. A comprehensive dataset comprising a diverse range of both malignant and benign skin lesion images is employed, with data augmentation techniques implemented to address class imbalance and enhance overall model robustness. Experimental results demonstrate that our model achieves high performance across key metrics such as accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC-ROC), outperforming traditional diagnostic methods. The integration of this deep learning approach into clinical workflows has the potential to significantly improve diagnostic speed and accuracy, ultimately contributing to better patient outcomes. Future work will focus on further validation in clinical settings, exploring model interpretability, and enhancing the integration of the system into real-world diagnostic processes.