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KMID : 1151820220160010001
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Journal of the Korean Society of Radiology
2022 Volume.16 No. 1 p.1 ~ p.6
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Evaluation of Artificial Intelligence Accuracy by Increasing the CNN Hidden Layers: Using Cerebral Hemorrhage CT Data
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Kim Han-Jun
Kang Min-Ji
Kim Eun-Ji
Na Yong-Hyeon
Park Jae-Hee
Baek Su-Eun
Sim Su-Man
Hong Joo-Wan
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Abstract
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Deep learning is a collection of algorithms that enable learning by summarizing the key contents of large amounts of data; it is being developed to diagnose lesions in the medical imaging field. To evaluate the accuracy of the cerebral hemorrhage diagnosis, we used a convolutional neural network (CNN) to derive the diagnostic accuracy of cerebral parenchyma computed tomography (CT) images and the cerebral parenchyma CT images of areas where cerebral hemorrhages are suspected of having occurred. We compared the accuracy of CNN with different numbers of hidden layers and discovered that CNN with more hidden layers resulted in higher accuracy. The analysis results of the derived CT images used in this study to determine the presence of cerebral hemorrhages are expected to be used as foundation data in studies related to the application of artificial intelligence in the medical imaging industry.
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KEYWORD
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AI, CNN, hidden layer, Computed Tomography, Cerebral Hemorrhage
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