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A novel hybrid Extreme Learning Machine and Teaching–Learning-Based Optimization algorithm for skin cancer detection

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Healthcare Analytics 2023年 3卷 100161-100161页

作者： Priyadharshini, N. Selvanathan, N. Hemalatha, B. Sureshkumar, c. Department of Computer Science Sri GVG Visalakshi College for Women Udumalpet India Department of Information Technology Sona College of Technology Salem India Department of Information Technology KGiSL Institute of Technology Coimbatore India

Skin cancers, such as melanoma, can be difficult to spot in their early stages because they often resemble benign moles. Early detection of melanoma is crucial as it increases the chances of successful treatment and prevents cancer from spreading to other areas of the body. Machine learning algorithms and computer vision techniques are versatile techniques for detecting melanoma. However, current research has limitations, such as inaccurate detection and longer computation times. This paper proposes a novel hybrid Extreme Learning Machine (ELM) and Teaching–Learning-Based Optimization (TLBO) algorithm as a versatile technique for detecting melanoma. ELM is a single-hidden layer feed-forward neural network that can be trained quickly and accurately, while TLBO is an optimization algorithm used to fine-tune the network's parameters for improved performance. Together, these techniques can classify skin lesions as benign or malignant images, potentially improving melanoma detection accuracy. © 2023 The Author(s)

关键词： Extreme learning classifier improved weighted fuzzy c-means algorithm Neural networks Segmentation Skin cancer Teaching learning based optimization

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