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

TL;DR

This paper presents a novel AI-based method combining neuro-fuzzy and colonial competition algorithms for skin cancer diagnosis from dermatoscopic images, achieving high accuracy and aiding early detection.

Contribution

It introduces a new fusion approach using neuro-fuzzy and colonial competition algorithms for improved skin cancer diagnosis in dermoscopic images.

Findings

Achieved 94% accuracy on ISIC dermoscopic dataset.

Demonstrated effectiveness of the combined AI approach in skin cancer detection.

Contributed to early melanoma diagnosis with potential clinical impact.

Abstract

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.