METAHEURISTIC-ASSISTED HYBRID DEEP LEARNING FRAMEWORK FOR SKIN CANCER DETECTION, SEGMENTATION, CLASSIFICATION, AND SEVERITY PREDICTION
DOI:
https://doi.org/10.63503/c.acset.2025.18Keywords:
Skin Disease Detection, Classification, Preprocessing, Dermoscopic Images, Deep LearningAbstract
Skin cancer constitutes the most prevalent form of carcinoma, posing a significant threat to public health, with melanoma recognized for its particularly high mortality rate. The timely recognition of this pathology is crucial for the enactment of effective treatment modalities; nevertheless, traditional diagnostic techniques often face challenges due to deficiencies in image clarity and the complexities inherent in visual discrimination. This study introduces an advanced deep learning approaches designed to achieve superior segmentation and categorization of cutaneous neoplasms, with a distinct focus on the assessment of severity. Furthermore, the research includes a comprehensive analysis of the severity classifications of the identified cancers. Altogether, the potential of advanced deep learning methodologies to transform the landscape of skin cancer diagnostics is apparent, offering an integrative approach that enhances early detection, improves classification accuracy, and supports severity assessment, ultimately contributing to superior patient management and outcomes.
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