Authors : Dr. S. Kiran; Dr. R. Pradeep Kumar Reddy; M. Gowthami; S. Sai Sathvik; P. Deepika; G. Likitha; K. Sravanthi

Volume/Issue : Volume 11 - 2026, Issue 2 - February

Google Scholar : https://tinyurl.com/45r2wd96

Scribd : https://tinyurl.com/4whszyby

DOI : https://doi.org/10.38124/ijisrt/26feb510

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Over 190,000 people die each year due to skin cancer, which is one of the most common cancers in the world.Traditional methods used to detect skin cancer are prone to human errors and take time.The accuracy and efficiency of skin cancer detection are being improved by machine learning, which is making it a better area for research. This review is conducted on both traditional machine learning classifiers such as k-nearest neighbors, support vector machines, decision trees, artificial neural networks and recent deep learning architectures including convolutional neural networks, GoogLeNet, ResNet, DenseNet, EfficientNet, and MobileNet. Rather than focusing only on reported accuracy, this review compares methodological differences, data dependencies, and challenges. Deep learning models generally shows higher performance through automatic feature extraction. Their usage is limited by issues related to dataset bias, computational cost, and limited interpretability. These challenges show the need for efficient and interpretable AI-based systems that can be effectively integrated into clinical practice. This review emphasizes methodological differences and real-world deployment feasibility rather than reporting accuracy metrics alone.

Keywords : Skin Cancer Detection, Machine Learning, Deep Learning, Convolutional Neural Networks, Automatic Feature Extraction, Medical Image Analysis.

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