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Skin cancer detection using ensemble of machine learning and deep learning techniques

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Abstract

Skin cancer is one of the most common forms of cancer, which makes it pertinent to be able to diagnose it accurately. In particular, melanoma is a form of skin cancer that is fatal and accounts for 6 of every 7-skin cancer related death. Moreover, in hospitals where dermatologists have to diagnose multiple cases of skin cancer, there are high possibilities of false negatives in diagnosis. To avoid such incidents, there here has been exhaustive research conducted by the research community all over the world to build highly accurate automated tools for skin cancer detection. In this paper, we introduce a novel approach of combining machine learning and deep learning techniques to solve the problem of skin cancer detection. The deep learning model uses state-of-the-art neural networks to extract features from images whereas the machine learning model processes image features which are obtained after performing the techniques such as Contourlet Transform and Local Binary Pattern Histogram. Meaningful feature extraction is crucial for any image classification roblem. As a result, by combining the manual and automated features, our designed model achieves a higher accuracy of 93% with an individual recall score of 99.7% and 86% for the benign and malignant forms of cancer, respectively. We benchmarked the model on publicly available Kaggle dataset containing processed images from ISIC Archive dataset. The proposed ensemble outperforms both expert dermatologists as well as other state-of-the-art deep learning and machine learning methods. Thus, this novel method can be of high assistance to dermatologists to help prevent any misdiagnosis.

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Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

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Acknowledgments

The authors, thanks to all the reviewers of Multimedia Tools and Applications Journal for their constructive remarks and suggestions to improve the manuscript.

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Authors and Affiliations

  1. Department of Computer Science & Engineering, Indian Institute of Information Technology, Nagpur, India

    Jitendra V. Tembhurne & Tausif Diwan

  2. School of Electronics Engineering (SENSE), VIT Vellore University, Vellore, India

    Nachiketa Hebbar & Hemprasad Y. Patil

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  1. Jitendra V. Tembhurne
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  3. Hemprasad Y. Patil
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  4. Tausif Diwan
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Correspondence to Jitendra V. Tembhurne.

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Tembhurne, J.V., Hebbar, N., Patil, H.Y. et al. Skin cancer detection using ensemble of machine learning and deep learning techniques. Multimed Tools Appl 82, 27501–27524 (2023). https://doi.org/10.1007/s11042-023-14697-3

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