# Optimizing Neuro-Fuzzy and Colonial Competition Algorithms for Skin Cancer Diagnosis in Dermatoscopic Images

arXiv: https://arxiv.org/abs/2505.08886 · HTML: https://mlsanity.com/paper/2505.08886/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 22 references that are in the map.
- First posted on arXiv 13 May 2025.

## Builds on (most cited first, 3 of 3)

- [Dermatologist Level Dermoscopy Skin Cancer Classification Using Different Deep Learning Convolutional Neural Networks Algorithms](https://mlsanity.com/paper/1810.10348/) (2018)
- [Automatic Lesion Detection System (ALDS) for Skin Cancer Classification Using SVM and Neural Classifiers](https://mlsanity.com/paper/2003.06276/) (2020)
- [Artificial Intelligence-Based Image Classification for Diagnosis of Skin Cancer: Challenges and Opportunities](https://mlsanity.com/paper/1911.11872/) (2019)

## Built on by (most cited first, 2 of 2)

- [Unified AI for Accurate Audio Anomaly Detection](https://mlsanity.com/paper/2505.23781/) (2025)
- [Leakage Safe Graph Features for Interpretable Fraud Detection in Temporal Transaction Networks](https://mlsanity.com/paper/2603.06632/) (2026)

## Abstract (arXiv)

The rising incidence of skin cancer, coupled with limited public awareness and a shortfall in clinical expertise, underscores an urgent need for advanced diagnostic aids. Artificial Intelligence (AI) has emerged as a promising tool in this domain, particularly for distinguishing malignant from benign skin lesions. Leveraging publicly available datasets of skin lesions, researchers have been developing AI-based diagnostic solutions. However, the integration of such computer systems in clinical settings is still nascent. This study aims to bridge this gap by employing a fusion of image processing techniques and machine learning algorithms, specifically neuro-fuzzy and colonial competition approaches. Applied to dermoscopic images from the ISIC database, our method achieved a notable accuracy of 94% on a dataset of 560 images. These results underscore the potential of our approach in aiding clinicians in the early detection of melanoma, thereby contributing significantly to skin cancer diagnostics.