Comparative Analysis of Automated Melanoma Recognition Algorithms in Dermoscopy

This paper presents a comparative analysis of automated melanoma recognition algorithms in dermoscopy, highlighting the strengths and limitations of various state-of-the-art techniques. The study evaluates different image processing and machine learning approaches used for early melanoma detection, focusing on accuracy, sensitivity, specificity, and computational efficiency. Publicly available dermoscopic image datasets were utilized to ensure consistency in performance assessment. The results emphasize the critical role of data quality, algorithmic architecture, and preprocessing techniques in determining diagnostic performance. This analysis aims to guide future research toward more robust, interpretable, and clinically viable melanoma detection systems.