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

Prabira Kumar Sethy1, Santi Kumari Behera2, Nithiyanathan Kannan3

  • 1Department of Electronics, Sambalpur University, Sambalpur, Odisha, India. prabirasethy.05@gmail.com.

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Summary

This study introduces a new model for classifying common pigmented skin lesions (CPSL) to aid in skin cancer detection. Combining deep learning features with Support Vector Machines (SVM) achieved 91.7% accuracy in identifying CPSL.

Keywords:
Common Pigmented Skin LesionsConvolutional neural networkMulti-deep featuresSkin cancerSupport vector machine

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Area of Science:

  • Dermatology
  • Computer Science
  • Artificial Intelligence

Background:

  • Common pigmented skin lesions (CPSL) are primary causes of skin cancer.
  • Accurate classification of CPSL is challenging due to computational costs and overfitting.
  • Existing methods struggle with reliable categorization of pigmented skin lesions.

Purpose of the Study:

  • To develop an accurate and efficient classification model for common pigmented skin lesions (CPSL).
  • To improve the categorization of CPSL, reducing risks associated with skin cancer.
  • To explore the efficacy of multi-deep features combined with Support Vector Machine (SVM) for CPSL classification.

Main Methods:

  • A two-phase approach was proposed, evaluating 11 Convolutional Neural Network (CNN) models for deep feature extraction.
  • Top-performing CNN models' deep features were concatenated and classified using SVM.
  • Feature sets of 8192 and 12,288 were generated and analyzed with Principal Component Analysis (PCA).

Main Results:

  • The combination of deep features from Alexnet, VGG16, and VGG19 achieved the highest accuracy of 91.7% using an SVM classifier.
  • A feature set of 12,288 demonstrated superior performance compared to 8192 features.
  • The proposed multi-deep feature and SVM model proved effective for CPSL classification.

Conclusions:

  • The developed model offers a robust tool for the accurate classification of common pigmented skin lesions.
  • This approach can aid in early detection and management of skin cancer.
  • Combining deep learning features with SVM presents a promising direction for dermatological image analysis.