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Research Paper | Computer Science and Engineering | Volume 15 Issue 2, February 2026 | Pages: 738 - 745 | India
Melanoma Recognition through Dermoscopic Imaging and SVM Classification
Abstract: In today's modern world, skin cancer is one of the most common causes of death among humans. It is an abnormal growth of skin cells, often developing on areas exposed to sunlight, but it can also occur on parts not directly exposed. Fortunately, most skin cancers are curable if detected early. Therefore, early and fast detection can be life-saving. Traditional diagnosis methods, such as biopsy, involve removing skin tissue for laboratory testing. Though effective, these are painful, time-consuming, and costly. To address these issues, we propose a Skin Cancer Detection System using Support Vector Machine (SVM) for early and non-invasive diagnosis. The system leverages image processing techniques and SVM algorithms to improve detection speed and accuracy. Importantly, it also reduces unnecessary excisions of harmless moles and lesions through precise classification. The Skin Cancer Detection System is an innovative healthcare solution aimed at supporting early diagnosis, particularly of melanoma, the most dangerous form of skin cancer. By utilizing machine learning algorithms and advanced image processing, this system analyzes digital images of skin lesions and classifies them as benign or malignant. It is designed to provide a user-friendly and reliable platform for healthcare professionals, dermatologists, and individuals for proactive skin health monitoring.
Keywords: skin cancer detection, SVM classification, image processing, melanoma diagnosis, medical imaging
How to Cite?: Sulekha Kundu, "Melanoma Recognition through Dermoscopic Imaging and SVM Classification", Volume 15 Issue 2, February 2026, International Journal of Science and Research (IJSR), Pages: 738-745, https://www.ijsr.net/getabstract.php?paperid=SR251211120345, DOI: https://dx.dx.doi.org/10.21275/SR251211120345