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http://dx.doi.org/10.7742/jksr.2022.16.1.1

Evaluation of Artificial Intelligence Accuracy by Increasing the CNN Hidden Layers: Using Cerebral Hemorrhage CT Data  

Kim, Han-Jun (Department of Radiological Science, College of Health Sciences, Eulji University)
Kang, Min-Ji (Department of Radiological Science, College of Health Sciences, Eulji University)
Kim, Eun-Ji (Department of Radiological Science, College of Health Sciences, Eulji University)
Na, Yong-Hyeon (Department of Radiological Science, College of Health Sciences, Eulji University)
Park, Jae-Hee (Department of Radiological Science, College of Health Sciences, Eulji University)
Baek, Su-Eun (Department of Radiological Science, College of Health Sciences, Eulji University)
Sim, Su-Man (Department of Radiological Science, College of Health Sciences, Eulji University)
Hong, Joo-Wan (Department of Radiological Science, College of Health Sciences, Eulji University)
Publication Information
Journal of the Korean Society of Radiology / v.16, no.1, 2022 , pp. 1-6 More about this Journal
Abstract
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.
Keywords
AI; CNN; hidden layer; Computed Tomography; Cerebral Hemorrhage;
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Times Cited By KSCI : 2  (Citation Analysis)
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1 D. G. Shen, G. R. Wu, H. I. Suk, "Deep learning in medical image analysis", Annual Review of Biomedical Engineering, Vol. 19, No. 1, pp. 221-248, 2017.   DOI
2 Liu, K. L., Wu, T., Chen, P. T., "Deep learning to distinguish pancreatic cancer tissue from non-cancerous pancreatic tissue: a retrospective study with cross-racial external validation", The Lancet Digital Health, Vol. 2, No. 6, pp. e303-e313. 2020.   DOI
3 J. W. Yoon, S. J. Lee, C. Y. Song, Y. S. Kim, M. Y. Jung, S. I. Jeong, "A Study on Similar Trademark Search Model Using Convolutional Neural Networks", Management & Information Systems Review, Vol. 38, No. 3, pp. 55-80, 2019.   DOI
4 S. M. McKinney, M. Sieniek, V. Godbole, J. Godwin, N. Antropova, H. Ashrafian, T. Back, M. Chesus, G. C. Corrado, A. Darzi, M. Etemadi, F. Garcia-Vicente, F. J. Gilbert, M. Halling-Brown, D. Hassabis, S. Jansen, A. Karthikesalingam, C. J. Kelly, D. King, J. R. Ledsam, D. Melnick, H. Mostofi, L. Peng, J. Jay Reicher, B. Romera-Paredes, R. Sidebottom, M. Suleyman, D. Tse, K. C. Young, J. D. Fauw, S. Shetty, "International evaluation of an AI system for breast cancer screening", Nature, Vol. 577, No. 7788, pp. 89-94, 2020.   DOI
5 M. F. Mushtaq, M. Shahroz, A. M. Aseere, H. Shah, R. Majeed, D. Shehzad, A. Samad, "BHCNet: Neural Network-Based Brain Hemorrhage Classification Using Head CT Scan", IEEE Access, Vol. 9, pp. 113901-113916, 2021.   DOI
6 M. Harman, "The role of artificial intelligence in software engineering", 2012 First International Workshop on Realizing AI Synergies in Software Engineering (RAISE); IEEE, 2012.
7 J. Y. Hong, S. H. Park, Y. J. Jung, "Artificial Intelligence Based Medical Imaging: An Overview", Journal of Radiological Science and Technology, Korean Society of Radiological Science, Vol. 43, No. 3, pp. 195-208. 2020.   DOI
8 H. L. Jin, "TransHarDNet: Intracerebral Hemorrhage Segmentation with Transformers", A thesis with a master's degree in Korea, The Graduate School Sejong University, 2021.
9 S. Y. Choi, S. S. Kang, C. S. Kim, J. H. Kim, D. H. Kim, S. Y. Ye, S. J. Ko, "Intracerebral hemorrhage auto recognition in computed tomography images", Journal of radiological science and technology, Vol. 36, No. 2, pp. 141-148, 2013.
10 A. Gautam, B. Raman, "Towards effective classification of brain hemorrhagic and ischemic stroke using CNN", Biomedical Signal Processing and Control, Vol. 63, pp. 102178, 2021.   DOI
11 J. Hamwood, B. Schmutz, M. J. Collins, M. C. Allenby, D. Alonso-Caneiro, "A deep learning method for automatic segmentation of the bony orbit in MRI and CT images", Scientific Reports, Vol. 11, No. 1, pp. 1-12, 2021.   DOI
12 J. C. Trinder, Y. Wang, A. Sowmya, M. Palhang, "Artificial Intelligence in 3-D Feature Extraction", Automatic Extraction of Man-Made Objects from Aerial and Space Images (II), pp. 257-266, 1997.
13 A. Arab, B. Chinda, G. Medvedev, W. Siu, H. Guo, T. Gu, S. Moreno, G. Hamarneh, M. Ester, X. Song, "A fast and fully-automated deep-learning approach for accurate hemorrhage segmentation and volume quantification in non-contrast whole-head CT", Scientific Reports, Vol. 10, No. 1, pp. 1-12, 2020.   DOI
14 J. Y. Kim, S. Y. Ye, "Accuracy Evaluation of Brain Parenchymal MRI Image Classification Using Inception V3", Journal of the Institute of Convergence Signal Processing, Vol. 20, No. 3, pp. 132-137, 2019.
15 Y. Bengio, A. Courville, P. Vincent, "Representation Learning: A Review and New Perspectives," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 35, No. 8, pp. 1798-1828, 2013.   DOI