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Article

Feature Selection of Non-Dermoscopic Skin Lesion Images for Nevus and Melanoma Classification

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Department of Chemistry, Physics & Environment, Faculty of Sciences and Environment, Dunarea de Jos University of Galati, 47 Domneasca Str., 800008 Galati, Romania
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The Modelling & Simulation Laboratory, Dunarea de Jos University of Galati, 111 Domneasca Str., 800102 Galati, Romania
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Department of Computer Science and Information Technology, Faculty of Automation, Computers, Electrical Engineering and Electronics, Dunarea de Jos University of Galati, 47 Domneasca Str., 800008 Galati, Romania
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Department of Information Technology, Techno India College of Technology, West Bengal 700156, India
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Department of Electronics and Electrical Communications Engineering, Faculty of Engineering, Tanta University, Tanta 31527, Egypt
*
Author to whom correspondence should be addressed.
Received: 3 March 2020 / Revised: 3 April 2020 / Accepted: 29 April 2020 / Published: 30 April 2020
(1) Background: In this research, we aimed to identify and validate a set of relevant features to distinguish between benign nevi and melanoma lesions. (2) Methods: Two datasets with 70 melanomas and 100 nevi were investigated. The first one contained raw images. The second dataset contained images preprocessed for noise removal and uneven illumination reduction. Further, the images belonging to both datasets were segmented, followed by extracting features considered in terms of form/shape and color such as asymmetry, eccentricity, circularity, asymmetry of color distribution, quadrant asymmetry, fast Fourier transform (FFT) normalization amplitude, and 6th and 7th Hu’s moments. The FFT normalization amplitude is an atypical feature that is computed as a Fourier transform descriptor and focuses on geometric signatures of skin lesions using the frequency domain information. The receiver operating characteristic (ROC) curve and area under the curve (AUC) were employed to ascertain the relevance of the selected features and their capability to differentiate between nevi and melanoma. (3) Results: The ROC curves and AUC were employed for all experiments and selected features. A comparison in terms of the accuracy and AUC was performed, and an evaluation of the performance of the analyzed features was carried out. (4) Conclusions: The asymmetry index and eccentricity, together with F6 Hu’s invariant moment, were fairly competent in providing a good separation between malignant melanoma and benign lesions. Also, the FFT normalization amplitude feature should be exploited due to showing potential in classification. View Full-Text
Keywords: skin lesion; morphological operators; feature extraction; ROC curves; AUC skin lesion; morphological operators; feature extraction; ROC curves; AUC
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MDPI and ACS Style

Damian, F.A.; Moldovanu, S.; Dey, N.; Ashour, A.S.; Moraru, L. Feature Selection of Non-Dermoscopic Skin Lesion Images for Nevus and Melanoma Classification. Computation 2020, 8, 41. https://0-doi-org.brum.beds.ac.uk/10.3390/computation8020041

AMA Style

Damian FA, Moldovanu S, Dey N, Ashour AS, Moraru L. Feature Selection of Non-Dermoscopic Skin Lesion Images for Nevus and Melanoma Classification. Computation. 2020; 8(2):41. https://0-doi-org.brum.beds.ac.uk/10.3390/computation8020041

Chicago/Turabian Style

Damian, Felicia A., Simona Moldovanu, Nilanjan Dey, Amira S. Ashour, and Luminita Moraru. 2020. "Feature Selection of Non-Dermoscopic Skin Lesion Images for Nevus and Melanoma Classification" Computation 8, no. 2: 41. https://0-doi-org.brum.beds.ac.uk/10.3390/computation8020041

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