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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. [ABSTRACT FROM AUTHOR]

Case-Based Reasoning (CBR) system maintenance is an important issue for current medical systems research. Large-scale CBR systems are becoming more omnipresent, with immense case libraries consisting of millions of cases. Case-Base Maintenance (CBM) is the implementation of the following policies allowing to revise the organization and/or the content (information content, representation field of application, or the implementation) of the Case Base (CB) to improve future thinking. Diverse case-base deletion and addition policies have been proposed which claim to preserve case-base competence. This paper presents a novel clustering-based deletion policy for CBM that exploits the K-means clustering algorithm. Thus, CBM becomes a central subject whose objective is to guarantee the quality of the CB and improve the performance of CBM. The proposed approach exploited clustering, which groups similar cases using the K-means algorithm. We rely on the characterization made of the different cases in the CB, and we find this characterization by a method based on a criterion of competence and performance. From this categorization, case deletion becomes obvious. This quality depends on the competence and performance of the CB. Test results show that the proposed deletion strategy improved the efficiency of the CB while preserving competence.Furthermore, its performance was 13% more reliable. The effectiveness of the proposed approach examined on the medical databases and its performance has been compared with the existing approaches on deletion policy. Experimental results are very encouraging. [ABSTRACT FROM AUTHOR]