A Review on Deep Convolutional Neural Networks for Accurate Skin Cancer Detection and Classification
Volume: 14 - Issue: 01 - Date: 01-01-2025
Approved ISSN: 2278-1412
Published Id: IJAECESTU443 | Page No.: 136-140
Author: Cholke Dnyaneshwar Ramdas
Co- Author: Dr. Tripti Arjariya
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.
Key Words:-Skin Cancer Detection, Deep Learning, Convolutional Neural Networks (CNN), Multiclass Classification, Medical Image Analysis, Dermoscopy, Automated Diagnosis, Melanoma, ResNet, VGGNet
Area:-Engineering
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