A Review on Deep Convolutional Neural Networks for Accurate Skin Cancer Detection and Classification

Abstract:-Skin cancer remains one of the most prevalent forms of cancer globally, with early and accurate diagnosis being critical for effective treatment and survival. Recent advancements in deep learning, particularly Convolutional Neural Networks (CNNs), have revolutionized the field of medical image analysis by enabling automated, accurate, and efficient detection of various skin cancer types. This review comprehensively explores the application of CNN-based models for multiclass skin cancer classification, focusing on the architectural innovations, datasets, preprocessing techniques, and performance evaluation metrics used in the literature. The paper highlights how deep CNNs outperform traditional image processing methods by learning complex features directly from dermoscopic images. Various CNN architectures, including AlexNet, VGG, ResNet, DenseNet, and custom hybrid models, are compared in terms of accuracy, sensitivity, specificity, and computational efficiency. Additionally, challenges such as class imbalance, data scarcity, model interpretability, and the need for real-time diagnosis are discussed. The review concludes by identifying research gaps and suggesting future directions for integrating deep learning models into clinical workflows for robust and scalable skin cancer diagnosis.

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