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http://dx.doi.org/10.9717/kmms.2019.22.8.832

Analysis of Texture Features and Classifications for the Accurate Diagnosis of Prostate Cancer  

Kim, Cho-Hee (Dept of Digital Anti-Aging Healthcare, u-AHRC, Inje University)
So, Jae-Hong (Dept of Digital Anti-Aging Healthcare, u-AHRC, Inje University)
Park, Hyeon-Gyun (Dept of Computer Engineering, u-AHRC, Inje University)
Madusanka, Nuwan (Dept of Computer Engineering, u-AHRC, Inje University)
Deekshitha, Prakash (Dept of Computer Engineering, u-AHRC, Inje University)
Bhattacharjee, Subrata (Dept of Computer Engineering, u-AHRC, Inje University)
Choi, Heung-Kook (Dept of Computer Engineering, u-AHRC, Inje University)
Publication Information
Journal of Korea Multimedia Society / v.22, no.8, 2019 , pp. 832-843 More about this Journal
Abstract
Prostate cancer is a high-risk with a high incidence and is a disease that occurs only in men. Accurate diagnosis of cancer is necessary as the incidence of cancer patients is increasing. Prostate cancer is also a disease that is difficult to predict progress, so it is necessary to predict in advance through prognosis. Therefore, in this paper, grade classification is attempted based on texture feature extraction. There are two main methods of classification: Uses One-way Analysis of Variance (ANOVA) to determine whether texture features are significant values, compares them with all texture features and then uses only one classification i.e. Benign versus. The second method consisted of more detailed classifications without using ANOVA for better analysis between different grades. Results of both these methods are compared and analyzed through the machine learning models such as Support Vector Machine and K-Nearest Neighbor. The accuracy of Benign versus Grade 4&5 using the second method with the best results was 90.0 percentage.
Keywords
Prostate Cancer; Texture Feature; Support Vector Machine; K-Nearest Neighbor;
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