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).

用于皮肤癌诊断的增强版 Mud Ring Algorithm 和 Whale Optimization Algorithm 的 WMRA 将 bubble-net attack 和 mud ring strategy 应用于提取数字皮肤镜像，实证结果表明 WMRA 在适应度、峰值信噪比（PSNR）和均方误差（MSE）方面相对于其他近期方法具有强大的效果。

混合鲸泥环优化用于精确肤色癌症图像分割

Hybrid Whale-Mud-Ring Optimization for Precise Color Skin Cancer Image  Segmentation

Over the past few years, different computer-aided diagnosis (CAD) systems
have been proposed to tackle skin lesion analysis. Most of these systems work
only for dermoscopy images since there is a strong lack of public clinical
images archive available to design them. To fill this gap, we release a skin
lesion benchmark composed of clinical images collected from smartphone devices
and a set of patient clinical data containing up to 22 features. The dataset
consists of 1,373 patients, 1,641 skin lesions, and 2,298 images for six
different diagnostics: three skin diseases and three skin cancers. In total,
58.4% of the skin lesions are biopsy-proven, including 100% of the skin
cancers. By releasing this benchmark, we aim to aid future research and the
development of new tools to assist clinicians to detect skin cancer.

本研究针对皮肤病变分析提出计算机辅助诊断系统，提供临床图像数据、病人临床数据以及 6 种不同皮肤病变诊断分类的数据集，以期协助临床医生进行皮肤癌的检测。