Computer-aided Diagnosis of Melanoma: A Review of Existing Knowledge and Strategies

Author:

Maiti Ananjan1ORCID,Chatterjee Biswajoy2,Ashour Amira S.3,Dey Nilanjan1

Affiliation:

1. Department of Information Technology, Techno India College of Technology, Kolkata, India

2. Department of Computer Science and Engineering, University of Engineering and Management, Kolkata, India

3. Department of Electronics and Electrical Communications Engineering, Faculty of Engineering, Tanta University, Tanta, Egypt

Abstract

Computer-aided diagnosis (CAD) systems are the best alternative for immediate disclosure and diagnosis of skin diseases. Such systems comprise several image processing procedures, including segmentation, feature extraction and artificial intelligence (AI) based methods. This survey highlights different CAD methodologies for diagnosing Melanoma and related skin diseases. It has also discussed types, stages, treatments and various imaging techniques of skin cancer. Currently, researchers developed new techniques to detect each stage. Extensive studies on melanoma cancer detection were performed by incorporating advanced machine learning. Still, there is a high need for an accurate, faster, affordable, portable methodology for a CAD system. This will strengthen the work in related fields and address the future direction of a similar kind of research.

Publisher

Bentham Science Publishers Ltd.

Subject

Radiology, Nuclear Medicine and imaging

Cited by 7 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Skin lesion recognition via global-local attention and dual-branch input network;Engineering Applications of Artificial Intelligence;2024-01

2. Detection for melanoma skin cancer through ACCF, BPPF, and CLF techniques with machine learning approach;BMC Bioinformatics;2023-12-06

3. Hybrid Approach for the Design of CNNs Using Genetic Algorithms for Melanoma Classification;Pattern Recognition, Computer Vision, and Image Processing. ICPR 2022 International Workshops and Challenges;2023

4. Transfer Learning from ImageNet to the Domain of Pigmented Nevi;Artificial Intelligence and Soft Computing;2023

5. A Survey on Computer-Aided Intelligent Methods to Identify and Classify Skin Cancer;Informatics;2022-12-11

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