A Hybrid Deep Learning Approach for Skin Cancer Detection Using AlexNet and Region-Based Transfer Learning

Aakanksha Sahu; Mohd Shajid Anssari; Parineeta Jha

doi:10.36948/ijfmr.2025.v07i05.58392

A Hybrid Deep Learning Approach for Skin Cancer Detection Using AlexNet and Region-Based Transfer Learning

Author(s)	Ms. Aakanksha Sahu, Prof. Mohd Shajid Anssari, Ms. Parineeta Jha
Country	India
Abstract	One of the most deadly types of cancer is skin cancer. Unrepaired deoxyribonucleic acid (DNA) in skin cells results in genetic flaws or mutations on the skin, which is the cause of skin cancer. Early detection of skin cancer symptoms is necessary due to the rising incidence of the disease, its high death rate, and the high cost of treatment. For these problems, scientists have created a number of early skin cancer screening methods to resolve it. Symmetry, color, size, shape, and other lesion characteristics are utilized to identify skin cancer and differentiate it from melanoma. In order to identify skin cancer from dermoscopic images, this study proposes a hybrid deep learning model that combines AlexNet and region-based transfer learning. The method makes use of AlexNet's feature extraction capabilities and improves localization by utilizing region-based strategies like ROI pooling and Region Proposal Networks (RPN). A well selected dermoscopy dataset with data augmentation is used to train and assess the model. The integration of AlexNet with Region-Based Transfer Learning, which improves model interpretability and robustness by employing region proposals to focus on probable lesion sites.
Keywords	AlexNet, Region-based transfer learning, RPN, RoI pooling, Deep learning, Medical imaging.
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 7, Issue 5, September-October 2025
Published On	2025-10-19
DOI	https://doi.org/10.36948/ijfmr.2025.v07i05.58392

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A Hybrid Deep Learning Approach for Skin Cancer Detection Using AlexNet and Region-Based Transfer Learning

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