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A Novel Approach Using Transfer Learning Architectural Models Based Deep Learning Techniques for Identification and Classification of Malignant Skin Cancer

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Abstract

Melanoma, a form of skin cancer originating in melanocyte cells, poses a significant health risk, although it is less prevalent than other types of skin cancer. Its detection presents challenges, even under expert observation. To enhance the classification accuracy of skin lesions, a Deep Convolutional Neural Network, Visual Geometry Group model has been proposed. However, deep learning methods typically require substantial training time. To mitigate this, transfer learning techniques are employed, reducing training duration. Data sets sourced from the International Skin Imaging Collaboration are utilized to train the model within this proposed approach. Evaluation of classification performance involves metrics such as Accuracy, Positive Predictive Value, Negative Predictive Value, Specificity, and Sensitivity. The classifier’s performance on test data is depicted through a confusion matrix. The introduction of transfer learning techniques into the Deep Convolutional Neural Network has resulted in an improved classification accuracy of 85%, compared to the 81% achieved by a standard Convolutional Neural Network.

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Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

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Funding

There was no financial support received from any organization for carrying out this work.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Kongu Engineering College (Autonomous), Perundurai, Erode, Tamil Nadu, India

    Balambigai Subramanian, Elavarasi Kasirajan, Abarna Marimuthu & Abinaya Ravi

  2. Department of Electrical and Electronics Engineering, Kongu Engineering College (Autonomous), Perundurai, Erode, Tamil Nadu, India

    Suresh Muthusamy

  3. Department of Electrical and Electronics Engineering, Sri Krishna College of Engineering and Technology (Autonomous), Coimbatore, Tamil Nadu, India

    Kokilavani Thangaraj

  4. Department of Mechanical Engineering, Government Engineering College, Patan, Gujarat, India

    Hitesh Panchal

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  1. Balambigai Subramanian
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  2. Suresh Muthusamy
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  5. Elavarasi Kasirajan
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Contributions

All the authors contributed equally for the preparation of the manuscript. Balambigai Subramanian, Suresh Muthusamy—Preparation of manuscript. Kokilavani Thangaraj, Hitesh Panchal—Overall supervision. Elavarasi Kasirajan, Abarna Marimuthu, Abinaya Ravi—Data collection, experimentation.

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Correspondence to Suresh Muthusamy.

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Subramanian, B., Muthusamy, S., Thangaraj, K. et al. A Novel Approach Using Transfer Learning Architectural Models Based Deep Learning Techniques for Identification and Classification of Malignant Skin Cancer. Wireless Pers Commun 134, 2183–2201 (2024). https://doi.org/10.1007/s11277-024-11006-5

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