The multi-level image segmentation in dermatology application using an enhance Secretary Bird Optimization Algorithm

Dermatological diseases are prevalent globally and provide significant challenges in diagnosis and treatment. Dermatology has changed due to developments in high-resolution digital photography and medical imaging, making it possible to document and analyze skin, nail, and hair diseases in great detail. With more than 10,000 photos, the Skin Condition Image Network (SCIN) dataset has become an essential tool in this area. In dermatological image analysis, image segmentation is essential because it makes it easier to identify and classify areas of interest for use, including automated disease diagnosis, lesion identification, and measurement. However, because skin textures vary, lighting varies, and skin disorders appear differently individually, it is difficult to achieve reliable segmentation in dermatological images. While segmentation techniques are now helpful for broad image analysis jobs, they are frequently insufficient for dermatological images from datasets such as SCIN. Reliable and consistent segmentation results are hampered by problems such as uneven lighting, different lesion scales, and image artifacts. Therefore, particular optimization algorithms that can adapt to the unique characteristics of dermatological images are needed to increase segmentation accuracy. This work is designed explicitly for SCIN dermatological images, suggesting an enhanced multilevel image segmentation optimization method. Opposition-Based Learning (OBL) and Orthogonal Learning (OL) are two improvements that the Enhanced Secretary Bird Optimization Algorithm (mSBOA) uses to increase segmentation accuracy, robustness to image artifacts, and computational efficiency. This study aims to improve optimization algorithms for robust multilevel feature segmentation in SCIN dataset dermatological images, mitigate problems such as overlapping textures and variable illumination, increase computational efficiency without sacrificing accuracy, and investigate possible clinical benefits of higher segmentation accuracy in automated dermatological diagnostics. Accurate segmentation can help create personalized treatment approaches, enhance patient outcomes, and lower diagnostic errors. Dermatologists gain from the wider adoption of AI-based healthcare solutions made possible by strong segmentation algorithms, especially in distant or underdeveloped areas. By increasing the potential for automated dermatological evaluations and enhancing diagnostic capacities, the study’s findings advance the field of dermatological image analysis.