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http://dx.doi.org/10.9717/kmms.2020.23.4.539

A Study on Deep Learning Binary Classification of Prostate Pathological Images Using Multiple Image Enhancement Techniques  

Park, Hyeon-Gyun (Dept of Computer Engineering, u-AHRC, Inje University)
Bhattacharjee, Subrata (Dept of Computer Engineering, u-AHRC, Inje University)
Deekshitha, Prakash (Dept of Computer Engineering, u-AHRC, Inje University)
Kim, Cho-Hee (Dept of Digital Anti-Aging Healthcare, u-AHRC, Inje University)
Choi, Heung-Kook (Dept of Computer Engineering, u-AHRC, Inje University)
Publication Information
Journal of Korea Multimedia Society / v.23, no.4, 2020 , pp. 539-548 More about this Journal
Abstract
Deep learning technology is currently being used and applied in many different fields. Convolution neural network (CNN) is a method of artificial neural networks in deep learning, which is commonly used for analyzing different types of images through classification. In the conventional classification of histopathology images of prostate carcinomas, the rating of cancer is classified by human subjective observation. However, this approach has produced to some misdiagnosing of cancer grading. To solve this problem, CNN based classification method is proposed in this paper, to train the histological images and classify the prostate cancer grading into two classes of the benign and malignant. The CNN architecture used in this paper is based on the VGG models, which is specialized for image classification. However, color normalization was performed based on the contrast enhancement technique, and the normalized images were used for CNN training, to compare the classification results of both original and normalized images. In all cases, accuracy was over 90%, accuracy of the original was 96%, accuracy of other cases was higher, and loss was the lowest with 9%.
Keywords
Deep Learning; Convolutional Neural Network; VGGNet; Prostate Cancer; Image Enhancement; Contrast Enhancement;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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