An Approach in Melanoma Skin Cancer Segmentation With Bat Optimization Algorithm

Abstract

Numerous advancements and significant progress have been made in computer methods for medical applications, alongside technological developments. Automatic image analysis plays a crucial role in the realm of medical diagnosis and therapy. Recent breakthroughs, especially in the field of medical image processing, have enabled the automatic detection of various characteristics, alterations, diseases, and degenerative conditions using skin scans. Utilizing image processing methods, skin image analysis is instrumental in the identification and monitoring of conditions manifesting through alterations in skin structure. Notably, accurate segmentation of cancerous regions from the background remains a challenging task in the area of melanoma image analysis. The primary objective of this study is to achieve exceptional precision in delineating melanoma boundaries. Leveraging the Bat Optimization algorithm, we determine the optimal threshold for melanoma segmentation, effectively identifying the most accurate cancerous area boundaries. To evaluate the results, standard metrics such as accuracy, sensitivity, specificity, Dice coefficient, and F1 score are employed. In this study, we applied the Bat Optimization algorithm to determine the optimal threshold value for segmenting melanoma skin cancer, effectively identifying the most accurate cancerous area boundaries. For result evaluation, we employed standard metrics including accuracy, sensitivity, specificity, Dice coefficient, and F1 score, which yielded impressive values of 99.8%, 98.99%, 98.87%, 98.45%, and 98.24%, respectively.