研究生: |
徐唯毓 Wei-Yu Hsu |
---|---|
論文名稱: |
基於三維卷積神經網路於心房顫動病人的斷層掃描影像之中風預測演算法 Stroke Prediction Algorithm Based on 3D Convolutional Neural Network for CT Scans in Patients with Atrial Fibrillation |
指導教授: |
呂政修
Jenq-Shiou Leu |
口試委員: |
呂政修
Jenq-Shiou Leu 蔡佳醍 Chia-Ti Tsai 陳維美 Wei-Mei Chen 力博宏 Po-Hung Li |
學位類別: |
碩士 Master |
系所名稱: |
電資學院 - 電子工程系 Department of Electronic and Computer Engineering |
論文出版年: | 2023 |
畢業學年度: | 112 |
語文別: | 英文 |
論文頁數: | 27 |
中文關鍵詞: | 心房顫動 、栓塞性中風 、人工智慧 、深度學習 、3D卷積神經網絡 、心臟CT |
外文關鍵詞: | Atrial fibrillation, Embolic stroke, Artificial intelligence, Deep learning, 3D convolutional neural network, Cardiac CT |
相關次數: | 點閱:31 下載:0 |
分享至: |
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報 |
[1] S. S. Yadav and S. M. Jadhav, “Deep convolutional neural network based medical image classification for disease diagnosis,” Journal of Big data, vol. 6, no. 1, pp. 1–18, 2019.
[2] D. Sarwinda, R. H. Paradisa, A. Bustamam, and P. Anggia, “Deep learning in image classification using residual network (resnet) variants for detection of colorectal cancer,” Procedia Computer Science, vol. 179, pp. 423–431, 2021.
[3] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778, 2016.
[4] E.-L. Chen, P.-C. Chung, C.-L. Chen, H.-M. Tsai, and C.-I. Chang, “An automatic diagnostic system for ct liver image classification,” IEEE Transactions on Biomedical Engineering, vol. 45, no. 6, pp. 783–794, 1998.
[5] X. W. Gao, R. Hui, and Z. Tian, “Classification of ct brain images based on deep learning networks,” Computer methods and programs in biomedicine, vol. 138, pp. 49–56, 2017.
[6] G. Litjens, T. Kooi, B. E. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, J. A. Van Der Laak, B. Van Ginneken, and C. I. Sánchez, “A survey on deep learning in medical image analysis,” Medical image analysis, vol. 42, pp. 60–88, 2017.
[7] H. Mzoughi, I. Njeh, A. Wali, M. B. Slima, A. BenHamida, C. Mhiri, and K. B. Mahfoudhe, “Deep multi-scale 3d convolutional neural network (cnn) for mri gliomas brain tumor classification,” Journal of Digital Imaging, vol. 33, pp. 903–915, 2020.
[8] Y. Li, H. Zhang, and Q. Shen, “Spectral–spatial classification of hyperspectral imagery with 3d convolutional neural network,” Remote Sensing, vol. 9, no. 1, p. 67, 2017.
[9] X. Li, Y. Zhou, P. Du, G. Lang, M. Xu, and W. Wu, “A deep learning system that generates quantitative ct reports for diagnosing pulmonary tuberculosis,” Applied Intelligence, vol. 51, pp. 4082–4093, 2021.
[10] N. Ganatra, “A comprehensive study of applying object detection methods for medical image analysis,” in 2021 8th International Conference on Computing for Sustainable Global Development (INDIACom), pp. 821–826, 2021.
[11] R. A. Welikala, P. Remagnino, J. H. Lim, C. S. Chan, S. Rajendran, T. G. Kallarakkal, R. B. Zain, R. D. Jayasinghe, J. Rimal, A. R. Kerr, R. Amtha, K. Patil, W. M. Tilakaratne, J. Gibson, S. C. Cheong, and S. A. Barman, “Automated detection and classification of oral lesions using deep learning for early
detection of oral cancer,” IEEE Access, vol. 8, pp. 132677–132693, 2020.
[12] G. Jocher, A. Chaurasia, A. Stoken, J. Borovec, NanoCode012, Y. Kwon, K. Michael, TaoXie, J. Fang, imyhxy, Lorna, Z. Yifu, C. Wong, A. V, D. Montes, Z. Wang, C. Fati, J. Nadar, Laughing, UnglvKitDe, 26 V. Sonck, tkianai, yxNONG, P. Skalski, A. Hogan, D. Nair, M. Strobel, and M. Jain, “ultralytics/yolov5: v7.0 - YOLOv5 SOTA Realtime Instance Segmentation,” Nov. 2022.
[13] P. Bourke, “Interpolation methods,” Miscellaneous: projection, modelling, rendering, vol. 1, no. 10, 1999.
[14] A. Gamal, M. Elattar, and S. Selim, “Automatic early diagnosis of alzheimer’s disease using 3d deep ensemble approach,” IEEE Access, vol. 10, pp. 115974–115987, 2022.
[15] P. M. Gordaliza, J. J. Vaquero, S. Sharpe, F. Gleeson, and A. Munoz-Barrutia, “A multi-task self-normalizing 3d-cnn to infer tuberculosis radiological manifestations,” arXiv preprint arXiv:1907.12331, 2019.
[16] J. Yang, X. Huang, Y. He, J. Xu, C. Yang, G. Xu, and B. Ni, “Reinventing 2d convolutions for 3d images,” IEEE Journal of Biomedical and Health Informatics, vol. 25, no. 8, pp. 3009–3018, 2021.
[17] S. Kazlouski, “Imageclef 2019: Ct image analysis for tb severity scoring and ct report generation using autoencoded image features.,” CLEF (Working Notes), vol. 2, 2019.
[18] S. Parveen, “Faster image zooming using cubic spline interpolation method,” International Journal on Recent and Innovation Trends in Computing and Communication, vol. 3, pp. 22–26, 2015.
[19] W. Alakwaa, M. Nassef, and A. Badr, “Lung cancer detection and classification with 3d convolutional neural network (3d-cnn),” International Journal of Advanced Computer Science and Applications, vol. 8, no. 8, 2017.
[20] K. Kamnitsas, C. Ledig, V. F. Newcombe, J. P. Simpson, A. D. Kane, D. K. Menon, D. Rueckert, and B. Glocker, “Efficient multi-scale 3d cnn with fully connected crf for accurate brain lesion segmentation,” Medical image analysis, vol. 36, pp. 61–78, 2017.
[21] D. Maturana and S. Scherer, “Voxnet: A 3d convolutional neural network for real-time object recognition,” in 2015 IEEE/RSJ international conference on intelligent robots and systems (IROS), pp. 922–928, IEEE, 2015.
[22] S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” in International conference on machine learning, pp. 448–456, pmlr, 2015.
[23] G. E. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, and R. R. Salakhutdinov, “Improving neural networks by preventing co-adaptation of feature detectors,” arXiv preprint arXiv:1207.0580, 2012.
[24] D. Mason, “Su-e-t-33: pydicom: an open source dicom library,” Medical Physics, vol. 38, no. 6Part10, pp. 3493–3493, 2011.
[25] K. Somasundaram and P. Kalavathi, “Medical image contrast enhancement based on gamma correction,” Int J Knowl Manag e-learning, vol. 3, no. 1, pp. 15–18, 2011.
[26] R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, “Grad-cam: Visual explanations from deep networks via gradient-based localization,” in Proceedings of the IEEE international conference on computer vision, pp. 618–626, 2017.