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http://dx.doi.org/10.9717/kmms.2022.25.8.1203

Melanoma Classification Using Log-Gabor Filter and Ensemble of Deep Convolution Neural Networks  

Long, Hoang (Department of Artificial Intelligence Convergence, Pukyong National University)
Lee, Suk-Hwan (Dept. of Computer Engineering, Donga University)
Kwon, Seong-Geun (Department of Electronics Engineering, Kyungil University)
Kwon, Ki-Ryong (Department of Artificial Intelligence Convergence, Pukyong National University)
Publication Information
Journal of Korea Multimedia Society / v.25, no.8, 2022 , pp. 1203-1211 More about this Journal
Abstract
Melanoma is a skin cancer that starts in pigment-producing cells (melanocytes). The death rates of skin cancer like melanoma can be reduced by early detection and diagnosis of diseases. It is common for doctors to spend a lot of time trying to distinguish between skin lesions and healthy cells because of their striking similarities. The detection of melanoma lesions can be made easier for doctors with the help of an automated classification system that uses deep learning. This study presents a new approach for melanoma classification based on an ensemble of deep convolution neural networks and a Log-Gabor filter. First, we create the Log-Gabor representation of the original image. Then, we input the Log-Gabor representation into a new ensemble of deep convolution neural networks. We evaluated the proposed method on the melanoma dataset collected at Yonsei University and Dongsan Clinic. Based on our numerical results, the proposed framework achieves more accuracy than other approaches.
Keywords
Melanoma Classification; Log-Gabor Filter; Ensemble Deep Convolution Neural Networks;
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Times Cited By KSCI : 5  (Citation Analysis)
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