Hybrid Whale-Mud-Ring Optimization for Precise Color Skin Cancer Image
  Segmentation
Amir Hamza Badis Lekouaghet Yassine Himeur
Timely identification and treatment of rapidly progressing skin cancers can
significantly contribute to the preservation of patients' health and
well-being. Dermoscopy, a dependable and accessible tool, plays a pivotal role
in the initial stages of skin cancer detection. Consequently, the effective
processing of digital dermoscopy images holds significant importance in
elevating the accuracy of skin cancer diagnoses. Multilevel thresholding is a
key tool in medical imaging that extracts objects within the image to
facilitate its analysis. In this paper, an enhanced version of the Mud Ring
Algorithm hybridized with the Whale Optimization Algorithm, named WMRA, is
proposed. The proposed approach utilizes bubble-net attack and mud ring
strategy to overcome stagnation in local optima and obtain optimal thresholds.
The experimental results show that WMRA is powerful against a cluster of recent
methods in terms of fitness, Peak Signal to Noise Ratio (PSNR), and Mean Square
Error (MSE).