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A Dermoscopic Skin Lesion Classification Technique Using YOLO-CNN and Traditional Feature Model

  • Research Article-Electrical Engineering
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Abstract

Skin cancer is one of the most deadly diseases around the world, wherein one of the three cancers is skin cancer. Early detection of skin cancer is paramount for better treatment planning. This paper investigates a Convolutional Neural Network (CNN), specifically, You Only Look Once (YOLO), to extract features from the skin lesions. The features, obtained from the CNN, are concatenated with traditional features like texture and colour features extracted from the lesion region of the input images. Later, the concatenated features are fed to a Fully Connected Network, which is trained with the specific ground truths to achieve higher classification accuracy. The proposed method improves the detection and classification of skin lesions when compared with other models and YOLO without traditional features. The performance measures of the fusion network are able to achieve the accuracy of 94%, precision of 0.85, recall of 0.88, and area under the curve of 0.95.

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Acknowledgements

The authors would like to thank Memorial Sloan-Kettering Cancer Center for their contribution in establishing the ISIC Archive. We would also like to thank Gutman et al. [40] for making their database openly available. The authors would like to thank Vellore Institute of Technology, Vellore, for providing the required support to carry out this research work.

Funding

This research was financially supported by the Scientific Research Grant of Shantou University, China, Grant No: NTF17016.

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Authors and Affiliations

  1. School of Electrical Engineering, Vellore Institute of Technology, Vellore, India

    Ruban Nersisson & Tharun J. Iyer

  2. Key Laboratory of Digital Signal and Image Processing of Guangdong Province, Department of Electronic Engineering, College of Engineering, Shantou University, Shantou, 515063, China

    Alex Noel Joseph Raj

  3. Centre for Healthcare Advancement, Innovation and Research, Vellore Institute of Technology, Chennai, India

    Vijayarajan Rajangam

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  1. Ruban Nersisson
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  2. Tharun J. Iyer
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  4. Vijayarajan Rajangam
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Correspondence to Alex Noel Joseph Raj.

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The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with animals performed by any of the authors. All procedures performed in studies involving human participants were in accordance with the ethical standards. The dataset used for experimentation is ISBI Melanoma dataset obtained from the 2016 International Skin Imaging Collaboration (ISIC) skin lesion classification challenge. The dataset is publicly available at https://www.isic-archive.com/#!/topWithHeader/onlyHeaderTop/gallery, and many researchers have used these images to experimentally verify and compare their works. As required we have also included the reference [40] which is related to their original publication. Further, we have also recognized their support by thanking them under the acknowledgment section.

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Nersisson, R., Iyer, T.J., Joseph Raj, A.N. et al. A Dermoscopic Skin Lesion Classification Technique Using YOLO-CNN and Traditional Feature Model. Arab J Sci Eng 46, 9797–9808 (2021). https://doi.org/10.1007/s13369-021-05571-1

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