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Automated Prediction of Malignant Melanoma using Two-Stage Convolutional Neural Network

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Abstract

Purpose

A skin lesion refers to an area of the skin that exhibits anomalous growth or distinctive visual characteristics compared to the surrounding skin. Benign skin lesions are noncancerous and generally pose no threat. These irregular skin growths can vary in appearance. On the other hand, malignant skin lesions correspond to skin cancer, which happens to be the most prevalent form of cancer in the United States. Skin cancer involves the unusual proliferation of skin cells anywhere on the body. The conventional method for detecting skin cancer is relatively more painful.

Methods

This work involves the automated prediction of skin cancer and its types using two stage Convolutional Neural Network (CNN). The first stage of CNN extracts low level features and second stage extracts high level features. Feature selection is done using these two CNN and ABCD (Asymmetry, Border irregularity, Colour variation, and Diameter) technique. The features extracted from the two CNNs are fused with ABCD features and fed into classifiers for the final prediction. The classifiers employed in this work include ensemble learning methods such as gradient boosting and XG boost, as well as machine learning classifiers like decision trees and logistic regression. This methodology is evaluated using the International Skin Imaging Collaboration (ISIC) 2018 and 2019 dataset.

Results

As a result, the first stage CNN which is used for creation of new dataset achieved an accuracy of 97.92%. Second stage CNN which is used for feature selection achieved an accuracy of 98.86%. Classification results are obtained for both with and without fusion of features.

Conclusion

Therefore, two stage prediction model achieved better results with feature fusion.

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Data availability

The datasets generated and/or analysed during the current work are available in the International Skin Imaging Collaboration (ISIC) repository, https://challenge.isic-archive.com/data/#2018 and https://challenge.isic-archive.com/data/#2019.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, India

    J. Angeline, A. Siva Kailash, J. Karthikeyan & R. Karthika

  2. Department of Computer Science, University of South Dakota, Vermillion, USA

    Vijayalakshmi Saravanan

Authors
  1. J. Angeline
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  2. A. Siva Kailash
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  3. J. Karthikeyan
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  4. R. Karthika
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  5. Vijayalakshmi Saravanan
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Contributions

All authors contributed equally to the study conception and design. Material preparation, data collection and analysis were performed by Angeline J, Siva Kailash A and Karthikeyan J. The first draft of the manuscript was written by Angeline J and Siva Kailash A and corrections were done by Karthika R and Vijayalakshmi Saravanan. All authors read and approved the final manuscript.

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Correspondence to R. Karthika.

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Angeline, J., Siva Kailash, A., Karthikeyan, J. et al. Automated Prediction of Malignant Melanoma using Two-Stage Convolutional Neural Network. Arch Dermatol Res 316, 275 (2024). https://doi.org/10.1007/s00403-024-03076-z

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  • DOI: https://doi.org/10.1007/s00403-024-03076-z

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