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Categorization of Common Pigmented Skin Lesions (CPSL) using Multi-Deep Features and Support Vector Machine

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Abstract

The skin is the main organ. It is approximately 8 pounds for the average adult. Our skin is a truly wonderful organ. It isolates us and shields our bodies from hazards. However, the skin is also vulnerable to damage and distracted from its original appearance: brown, black, or blue, or combinations of those colors, known as pigmented skin lesions. These common pigmented skin lesions (CPSL) are the leading factor of skin cancer, or can say these are the primary causes of skin cancer. In the healthcare sector, the categorization of CPSL is the main problem because of inaccurate outputs, overfitting, and higher computational costs. Hence, we proposed a classification model based on multi-deep feature and support vector machine (SVM) for the classification of CPSL. The proposed system comprises two phases: First, evaluate the 11 CNN model's performance in the deep feature extraction approach with SVM, and then, concatenate the top performed three CNN model's deep features and with the help of SVM to categorize the CPSL. In the second step, 8192 and 12,288 features are obtained by combining binary and triple networks of 4096 features from the top performed CNN model. These features are also given to the SVM classifiers. The SVM results are also evaluated with principal component analysis (PCA) algorithm to the combined feature of 8192 and 12,288. The highest results are obtained with 12,288 features. The experimentation results, the combination of the deep feature of Alexnet, VGG16 and VGG19, achieved the highest accuracy of 91.7% using SVM classifier. As a result, the results show that the proposed methods are a useful tool for CPSL classification.

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Data Availability Statement

Data sharing does not apply to this article, as no new data were created or analyzed in this study.

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Authors and Affiliations

  1. Department of Electronics, Sambalpur University, Sambalpur, Odisha, India

    Prabira Kumar Sethy

  2. Department of Computer Science and Engineering, Veer Surendra Sai University of Technology, Sambalpur, Odisha, India

    Santi Kumari Behera

  3. Department of Electrical Engineering, King Abdulaziz University, Rabigh, 560037, KSA, Saudi Arabia

    Nithiyanathan Kannan

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  1. Prabira Kumar Sethy
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Correspondence to Prabira Kumar Sethy.

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Sethy, P.K., Behera, S.K. & Kannan, N. Categorization of Common Pigmented Skin Lesions (CPSL) using Multi-Deep Features and Support Vector Machine. J Digit Imaging 35, 1207–1216 (2022). https://doi.org/10.1007/s10278-022-00632-9

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