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http://dx.doi.org/10.7742/jksr.2019.14.1.39

Evaluation of Classification and Accuracy in Chest X-ray Images using Deep Learning with Convolution Neural Network  

Song, Ho-Jun (Department of Radiological Science, Eulji University)
Lee, Eun-Byeol (Department of Radiological Science, Eulji University)
Jo, Heung-Joon (Department of Radiological Science, Eulji University)
Park, Se-Young (Department of Radiological Science, Eulji University)
Kim, So-Young (Department of Radiological Science, Eulji University)
Kim, Hyeon-Jeong (Department of Radiological Science, Eulji University)
Hong, Joo-Wan (Department of Radiological Science, Eulji University)
Publication Information
Journal of the Korean Society of Radiology / v.14, no.1, 2020 , pp. 39-44 More about this Journal
Abstract
The purpose of this study was learning about chest X-ray image classification and accuracy research through Deep Learning using big data technology with Convolution Neural Network. Normal 1,583 and Pneumonia 4,289 were used in chest X-ray images. The data were classified as train (88.8%), validation (0.2%) and test (11%). Constructed as Convolution Layer, Max pooling layer size 2×2, Flatten layer, and Image Data Generator. The number of filters, filter size, drop out, epoch, batch size, and loss function values were set when the Convolution layer were 3 and 4 respectively. The test data verification results showed that the predicted accuracy was 94.67% when the number of filters was 64-128-128-128, filter size 3×3, drop out 0.25, epoch 5, batch size 15, and loss function RMSprop was 4. In this study, the classification of chest X-ray Normal and Pneumonia was predictable with high accuracy, and it is believed to be of great help not only to chest X-ray images but also to other medical images.
Keywords
Deep Learning; CNN; Pneumonia; Chest X-Ray;
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