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

2024-03-15

International Congress on Information and Communication Technology

AI Summary

Rising global incidence of skin cancer, coupled with limited public awareness and clinical diagnostic resources, underscores the urgent need for reliable AI-assisted diagnosis. To address this, we propose a novel classification framework integrating image processing with intelligent optimization: (1) ISIC dermoscopic images undergo preprocessing and texture feature extraction; (2) an Adaptive Neuro-Fuzzy Inference System (ANFIS) serves as the core classifier, globally optimized for the first time using the Colonial Competitive Algorithm (CCA) to simultaneously enhance interpretability and generalization. Evaluated on 560 clinical cases, the framework achieves 94% classification accuracy—significantly outperforming conventional methods. This work extends the applicability of neuro-fuzzy systems to medical image analysis and establishes a high-accuracy, inherently interpretable AI paradigm for early-stage skin cancer screening.

Technology Category

• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Computer Vision: Learning & Optimization for CV
• Search and Optimization: Algorithm Configuration

Application Category

• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Optimizing AI algorithms for skin cancer diagnosis
• Improving accuracy in distinguishing malignant from benign lesions
• Bridging the gap in clinical AI integration for dermatology

Innovation

• Combines neuro-fuzzy and colonial competition algorithms
• Achieves 94% accuracy on ISIC dataset
• Enhances early melanoma detection in dermoscopy