Year: 2025
Type: article
Abstract: In modern years, Deep Learning (DL) systems, predominantly Convolutional Neural Networks (CNNs), have shown hopeful outcomes in computer-aided diagnosis and classification of skin lesions. In this paper, an advanced methodology for skin cancer detection is proposed that leverages preprocessing and classification-based multi-module CNN. The proposed method begins with preprocessing and data augmentation steps for improving the resolution of the skin lesions, including noise reduction and contrast enhancement, which optimize the input data for subsequent analysis. Subsequently, a multi-module CNN architecture is employed, consisting of interconnected modules designed to capture diverse features crucial for accurate classification. The overall proposed work is implemented in Python software, and a comparative analysis is carried out with the existing approaches to show the prominence of the developed work. The results of the investigation show that the model created has a high accuracy of 97% and a short classification time, which makes it a useful tool for helping dermatologists detect skin cancer early.
DOI: 10.14419/b1dzrb85
Subfield: Oncology
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4416257293
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