Skin Cancer Detection Using Machine Learning Based on Dermoscopic Images: A Systematic Literature Review
DOI:
https://doi.org/10.37253/joint.v6i2.10857Keywords:
Machine Learning, Deep Learning, Skin Cancer, Dermoscopic, Data Augmentation, Systematic Literature ReviewAbstract
Skin cancer, particularly melanoma, is a serious global health issue due to its aggressive nature and rising
incidence. Early and accurate detection is essential to improve patient outcomes, and recent advances in machine
learning (ML) and deep learning (DL) offer promising solutions through automated analysis of dermoscopic
images. This systematic literature review evaluates the performance of ML-based models, the impact of data
augmentation techniques, and the effectiveness of various algorithms using public datasets. The findings show that
convolutional neural networks (CNNs) dominate current approaches, with many models achieving high
accuracy—especially when enhanced with hybrid or ensemble methods. Data augmentation techniques such as
rotation, flipping, and brightness adjustment were found to improve model robustness and generalizability.
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