Advancing skin cancer research with machine learning and deep learning models: A systematic review
Keywords:
Skin cancer diagnosis, Machine Learning, Deep Learning, Medical research, Early detectionAbstract
Background The global impact of skin cancer has underscored the urgency of accurate and timely detection for effective treatment. In recent years, the medical research landscape has witnessed a rapid evolution fueled by the integration of Machine Learning (ML) and Deep Learning (DL) models, specifically aimed at enhancing skin cancer diagnosis and classification. Methods This comprehensive exploration delves into the forefront of advancements, focusing on the strategic application of ML and DL algorithms across critical facets of skin cancer management, encompassing detection, classification, and prognosis. By synthesizing diverse studies and emerging developments, this review aims to provide an all-encompassing perspective of the current landscape in skin cancer research, underpinned by the capabilities of ML and DL models. Results The synthesis of research outcomes within this review accentuates the remarkable progress achieved through the fusion of ML and DL methodologies. These achievements manifest as heightened accuracy and efficiency in skin cancer diagnosis and classification, offering invaluable support to healthcare professionals. The integration of these algorithmic approaches has ushered in improved patient outcomes, facilitating prompt interventions and tailored treatment strategies. Conclusions At its core, this review strives to equip researchers, clinicians, and healthcare providers with an intricate comprehension of the existing terrain in skin cancer research, driven by the prowess of ML and DL models. By spotlighting accomplishments alongside untapped prospects for refinement, this endeavor seeks to inspire fresh breakthroughs in the domain of skin cancer detection.References
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