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Multi-class multi-level classification algorithm for skin lesions classification using machine learning techniques
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DOI：10.1016/j.eswa.2019.112961.png)
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Abstract
En 中文
Skin diseases remain a major cause of disability worldwide and contribute approximately 1.79% of the global burden of disease measured in disability-adjusted life years. In the United Kingdom alone, 60% of the population suffer from skin diseases during their lifetime. In this paper, we propose an intelligent digital diagnosis scheme to improve the classification accuracy of multiple diseases. A Multi-Class Multi-Level (MCML) classification algorithm inspired by the divide and conquer rule is explored to address the research challenges. The MCML classification algorithm is implemented using traditional machine learning and advanced deep learning approaches. Improved techniques are proposed for noise removal in the traditional machine learning approach. The proposed algorithm is evaluated on 3672 classified images, collected from different sources and the diagnostic accuracy of 96.47% is achieved. To verify the performance of the proposed algorithm, its metrics are compared with the Multi-Class Single-Level classification algorithm which is the main algorithm used in most of the existing literature. The results also indicate that the MCML classification algorithm is capable of enhancing the classification performance of multiple skin lesions. (C) 2019 Elsevier Ltd. All rights reserved.
Keywords:
Skin lesion classification
Computer-aided diagnosise
Machine learning
Deep learning
Texture & colour features
Melanoma classification
Eczema classification
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