Navigating challenges in deep learning for skin cancer detection
DOI:
https://doi.org/10.20535/SRIT.2308-8893.2025.2.03Keywords:
skin cancer, deep learning, classification, transformers, CNN, GAN, data preprocessing, data augmentationAbstract
Skin cancer is one of the most prevalent malignancies worldwide. A critical factor in reducing mortality rates is the early detection. It underscores the need for accessible Computer-Aided Diagnostic (CAD) systems. Recent advancements in Deep Learning (DL) have shown great promise in addressing this challenge. Despite this progress in the field of machine learning, researchers encounter numerous obstacles when it comes to skin cancer classification. This article examines the current state of DL-based skin cancer diagnostics. Critical aspects of system development, including data preprocessing, model training, and performance evaluation, are addressed. Moreover, the article highlights opportunities for innovation that could significantly advance the field. By providing a comprehensive overview, this article aims to guide researchers and practitioners in optimizing DL models, addressing existing limitations, and exploring emerging trends to enhance diagnostic accuracy and accessibility.
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