Authors : Tanvir Mahmud; S A Sabbirul Mohosin Naim

Volume/Issue : Volume 10 - 2025, Issue 7 - July

Google Scholar : https://tinyurl.com/ys5c6pnx

DOI : https://doi.org/10.38124/ijisrt/25jul139

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Abstract : Melanoma is a highly fatal form of skin cancer, where early and accurate diagnosis plays a vital role in reducing mortality. Due to the striking similarities among different types of skin lesions, manual diagnosis remains challenging. Dermatologists rely on early-stage classification of skin lesions to administer timely treatment and save lives. This paper presents an effective deep learning-based classification model utilizing the VGG16 architecture through transfer learning. The proposed model successfully differentiates between benign and malignant skin lesions using a dataset comprising 1,800 benign and 1,498 malignant skin images collected from online sources. The model achieves a training accuracy of 99.62% and a validation accuracy of 84.97%, highlighting its potential for reliable clinical support.

Keywords : Skin Cancer Classification, Melanoma Detection, Deep Learning, Convolutional Neural Networks (CNN), VGG16, Transfer Learning, Dermoscopic Images.

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