Deep Learning-based Automated Skin Cancer Detection using Dermoscopic Images

A. Deno Star; Gibi Linza G; Siva Durshika G; Adchaya R T; Lakshmi Varnikha V K

Deep Learning-based Automated Skin Cancer Detection using Dermoscopic Images

Author(s)	Ms. A. Deno Star, Ms. Gibi Linza G, Ms. Siva Durshika G, Ms. Adchaya R T, Ms. Lakshmi Varnikha V K
Country	India
Abstract	Skin cancer is one of the most common and potentially life-threatening diseases worldwide, where early detection plays a crucial role in improving patient survival rates. Traditional diagnostic methods rely heavily on dermatologists’ expertise, which may lead to variability and delayed diagnosis. This study proposes a deep learning-based approach for accurate and efficient skin cancer prediction using dermoscopic images. Convolutional Neural Networks (CNNs), known for their powerful feature extraction capabilities, are employed to automatically learn complex patterns and distinguish between benign and malignant skin lesions. Preprocessing techniques such as image resizing, normalization, and data augmentation are applied to enhance model performance and reduce overfitting. The proposed model is trained and evaluated on publicly available datasets, achieving high accuracy, sensitivity, and specificity. The results demonstrate that deep learning techniques can significantly assist in early detection and classification of skin cancer, thereby supporting dermatologists in clinical decision-making. This approach has the potential to improve diagnostic efficiency and reduce mortality rates associated with skin cancer.
Keywords	Deep Learning, CNN, Preprocessing techniques
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 8, Issue 4, July-August 2026
Published On	2026-08-01

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International Journal For Multidisciplinary Research

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Deep Learning-based Automated Skin Cancer Detection using Dermoscopic Images

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