Melanoma Detection Using Deep Learning Techniques

Skin Cancer, particularly melanoma, is one of the most prevalent and deadly forms of cancer globally. Early detection significantly improves survival rates, but manual diagnosis by dermatologists is often subjective and time-consuming. This study proposes a deep learning-based approach to skin cancer prediction, focusing on the classification of malignant and benign lesions. Leveraging a convolutional neural network (CNN) architecture, the model is trained on a large dataset of dermoscopic images. The system automates feature extraction, reducing human bias and error. Our model achieves state-of-the-art performance by incorporating techniques such as data aug- mentation, transfer learning, and fine-tuning on pre-trained networks (such as ResNet & VGG ), which improve the model’s ability to generalize across diverse skin tones and lesion types. Evaluation metrics such as accuracy, sensitivity, specificity, and AUC (Area Under the Curve) are used to assess the model’s efficacy. The results demonstrate the potential of deep learning to en- hance early diagnosis, aiding dermatologists in clinical decision-making and providing a foundation for scalable, accessible skin cancer screening tools.