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

arXiv: https://arxiv.org/abs/2311.13512 · HTML: https://mlsanity.com/paper/2311.13512/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 28 references that are in the map.
- First posted on arXiv 22 Nov 2023.

## Builds on (most cited first, 4 of 4)

- [SkinSAM: Empowering Skin Cancer Segmentation with Segment Anything Model](https://mlsanity.com/paper/2304.13973/) (2023)
- [AI in Thyroid Cancer Diagnosis: Techniques, Trends, and Future Directions](https://mlsanity.com/paper/2308.13592/) (2023)
- [Nature-Inspired Optimization Algorithms: Challenges and Open Problems](https://mlsanity.com/paper/2003.03776/) (2020)
- [Harnessing Transformers: A Leap Forward in Lung Cancer Image Detection](https://mlsanity.com/paper/2311.09942/) (2023)

## Built on by (most cited first, 5 of 5)

- [Federated and Transfer Learning for Cancer Detection Based on Image Analysis](https://mlsanity.com/paper/2405.20126/) (2024)
- [Deep Transfer Learning for Kidney Cancer Diagnosis](https://mlsanity.com/paper/2408.04318/) (2024)
- [Machine Learning and Transformers for Thyroid Carcinoma Diagnosis: A Review](https://mlsanity.com/paper/2403.13843/) (2024)
- [Interpretable Deep Transfer Learning for Breast Ultrasound Cancer Detection: A Multi-Dataset Study](https://mlsanity.com/paper/2509.05004/) (2025)
- [A Novel Hybrid Deep Learning and Chaotic Dynamics Approach for Thyroid Cancer Classification](https://mlsanity.com/paper/2509.23968/) (2025)

## Abstract (arXiv)

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).