Automated skin cancer detection using deep learning techniques and image processing

Skin cancer is among the most common types and potentially fatal types of cancer globally, with early detection playing a critical role in improving patient survival rates. This study provides a deep learning-based examination of the most prevalent approach for automated skin cancer detection using the ResNet-50 convolutional neural network architecture, combined with advanced image processing techniques. The model is trained and evaluated on the publicly available ISIC dataset, which includes a wide variety of dermoscopic images representing both malignant and benign lesions. Preprocessing steps such as noise removal, resizing, and contrast enhancement are applied to improve image quality and feature extraction. The ResNet-50 model is fine-tuned using transfer learning, enabling accurate classification with relatively limited annotated data. The proposed system achieves high accuracy, precision, and recall in distinguishing between cancerous and non-cancerous skin lesions. The results demonstrate the effectiveness of deep residual networks in classification challenges for medical images and their potential to assist dermatologists in clinical decision-making.