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KMID : 1151820200140010039
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Journal of the Korean Society of Radiology
2020 Volume.14 No. 1 p.39 ~ p.44
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Evaluation of Classification and Accuracy in Chest X-ray Images using Deep Learning with Convolution Neural Network
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Song Ho-Jun
Lee Eun-Byeul
Jo Heung-Joon
Park Se-Young
Kim So-Young
Kim Hyeon-Jeong
Hong Joo-Wan
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Abstract
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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.
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KEYWORD
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Deep Learning, CNN, Pneumonia, Chest X-Ray
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