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An Integrated Ensemble Network Model for Skin Abnormality Detection with Combined Textural Features

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Abstract

Melanoma is the most lethal of all skin cancers. This necessitates the need for a machine learning-driven skin cancer detection system to help medical professionals with early detection. We propose an integrated multi-modal ensemble framework that combines deep convolution neural representations with extracted lesion characteristics and patient meta-data. This study intends to integrate transfer-learned image features, global and local textural information, and patient data using a custom generator to diagnose skin cancer accurately. The architecture combines multiple models in a weighted ensemble strategy, which was trained and validated on specific and distinct datasets, namely, HAM10000, BCN20000 + MSK, and the ISIC2020 challenge datasets. They were evaluated on the mean values of precision, recall or sensitivity, specificity, and balanced accuracy metrics. Sensitivity and specificity play a major role in diagnostics. The model achieved sensitivities of 94.15%, 86.69%, and 86.48% and specificity of 99.24%, 97.73%, and 98.51% for each dataset, respectively. Additionally, the accuracy on the malignant classes of the three datasets was 94%, 87.33%, and 89%, which is significantly higher than the physician recognition rate. The results demonstrate that our weighted voting integrated ensemble strategy outperforms existing models and could serve as an initial diagnostic tool for skin cancer.

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Availability of Data and Materials

The datasets are downloaded from the ISIC repository https://challenge.isic-archive.com/data/.

Code Availability

http://mirworks.in/downloads.php.

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Acknowledgements

The authors would like to extend gratitude to all researchers and doctors affiliated with the Machine Intelligence Research (MIR) Laboratory for their support during each phase of this work. The authors also thank University of Kerala for providing the infrastructure required for the study.

Funding

This work was supported by University Grants Commission, India with NTA Ref. No.: 200510450932.

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Authors and Affiliations

  1. Machine Intelligence Research Laboratory, Department of Computer Science, University of Kerala, Trivandrum, India

    Misaj Sharafudeen & Vinod Chandra S S

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  1. Misaj Sharafudeen
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  2. Vinod Chandra S S
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Misaj Sharafudeen. The first draft of the manuscript was written by Misaj Sharafudeen. Vinod Chandra SS reviewed and commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Vinod Chandra S S.

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Sharafudeen, M., S S, V.C. An Integrated Ensemble Network Model for Skin Abnormality Detection with Combined Textural Features. J Digit Imaging 36, 1723–1738 (2023). https://doi.org/10.1007/s10278-023-00837-6

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  • DOI: https://doi.org/10.1007/s10278-023-00837-6

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