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All Journal International Journal of Electrical and Computer Engineering
Mohamed Khaled Abu Mahmoud
University of Technology
Computer Science & IT Electrical & Electronics Engineering

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Journal : International Journal of Electrical and Computer Engineering

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PSO-SVM hybrid system for melanoma detection from histo-pathological images Maen Takruri; Mohamed Khaled Abu Mahmoud; Adel Al-Jumaily
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 4: August 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (712.709 KB) | DOI: 10.11591/ijece.v9i4.pp2941-2949

Abstract

This paper introduces an automated system for skin cancer (melanoma) detection from Histo-pathological images sampled from microscopic slides of skin biopsy. The proposed system is a hybrid system based on Particle Swarm Optimization and Support Vector Machine (PSO-SVM). The features used are extracted from the grayscale image histogram, the co-occurrence matrix and the energy of the wavelet coefficients resulting from the wavelet packet decomposition. The PSO-SVM system selects the best feature set and the best values for the SVM parameters (C and γ) that optimize the performance of the SVM classifier.   The system performance is tested on a real dataset obtained from the Southern Pathology Laboratory in Wollongong NSW, Australia. Evaluation results show a classification accuracy of 87.13%, a sensitivity of 94.1% and a specificity of 80.22%.The sensitivity and specificity results are comparable to those obtained by dermatologists.
Co-Authors Adel Al-Jumaily Maen Takruri