臺灣博碩士論文加值系統

第一章 緒論 1
第一節 研究背景與動機 1
第二節 研究目的 4
第二章 文獻探討 5
第一節 幽門螺旋桿菌感染簡介 5
一、 幽門螺旋桿菌致病機制 5
二、 幽門螺旋桿菌感染檢驗方式 6
第二節 深度學習簡介 9
一、 類神經網路 9
二、 卷積神經網路 16
三、 知名卷積神經網路 20
第三節 識別幽門桿菌感染之內視鏡影像相關研究探討 28
第四節 文獻小節 32
第三章 系統設計與方法 33
第一節 系統架構 35
第二節 資料庫建置 36
一、 研究樣本 36
二、 資料前處理 38
第三節 模型訓練 44
一、 Inception v3 44
二、 ResNet50 49
三、 Xception 51
四、 激勵函數 53
五、 損失函數 54
六、 最佳化決策函數 55
七、 轉移學習 57
第四節 資訊融合 59
第五節 環境與設備 60
第四章 研究結果 62
第一節 評估方法 62
第二節 Inception v3評估結果 65
第三節 ResNet50評估結果 70
第四節 Xception評估結果 75
第五節 三種模型結果比較 80
第六節 資訊融合結果 86
第五章 討論 101
第六章 結論 104
參考文獻 106

Boureau, Y., Ponce, J., & LeCun, Y. (2010). A theoretical analysis of feature pooling in visual recognition. Proceedings of the International Conference on Machine Learning (ICML), 111-118.
Boutaba, R., Salahuddin, M. A., Limam, N., Ayoubi, S., Shahriar, N., Solano, F. E., & Caicedo, O. M. (2018). A comprehensive survey on machine learning for networking: evolution, applications and research opportunities. Journal of Internet Services and Applications, 9(16), 1-99.
Bravo, D., Hoare, A., Soto, C., Valenzuela, M. A., & Quest, A. F. G. (2018). Helicobacter pylori in human health and disease: Mechanisms for local gastric and systemic effects. World Journal of Gastroenterology, 24(28), 3071-3089.
Canziani, A., Culurciello, E., & Paszke, A. (2017). An analysis of deep neural network models for practical applications. Computer Vision and Pattern Recognition, 1-7.
Chollet, F. (2017). Xception: Deep Learning with Depthwise Separable Convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1251-1258.
Chougrad, H., Zouaki, H., & Alheyane, O.(2018).Deep Convolutional Neural Networks for breast cancer screening. Computer Methods and Programs in Biomedicine, 157,19-30.
Faust, O., Hagiwara, Y., Hong, T. J., Lih, O. S., & Acharya, U. R. (2018). Deep learning for healthcare applications based on physiological signals: A review. Computer Methods and Programs in Biomedicine, 161, 1-13.
Fukushima, K. & Miyake, S. (1982). Neocognitron: a self-organizing neural network model for a mechanism of visual pattern recognition. Competition and Cooperation in Neural Nets, 267-285.
Goodfellow, I., Bengio, Y., & Courville, A.(2016). Deep Learning, MIT press.
Goodwin, C. S. (1988). Duodenal ulcer, Campylobacter pylori, and the “ leaking roof ” concept. Lancet, 2, 1467-1469.
Goodwin, C. S.(1997). Helicobacter pylori gastritis, peptic ulcer, and gastric cancer: clinical and molecular aspects. Clinical Infectious Diseases, 25(5), 1017 – 1019.
Halevy, A., Norvig, P., & Pereira, F. (2009). The unreasonable effectiveness of data. IEEE Computer Society, 1541-1672.
Han, J. & Morag, C. (1995). The influence of the sigmoid function parameters on the speed of backpropagation learning. Lecture Notes in Computer Science, 930, 195-201.
Hellström, P. M. (2006). This year’s Nobel Prize to gastroenterology: Robin Warren and Barry Marshall awarded for their discovery of Helicobacter pylori as pathogen in the gastrointestinal tract. World Journal of Gastroenterology, 12(19), 3126-3127.
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770-778.
Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., & Adam, H. (2017). MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR),1-9.
Huang, C. R., Chung, P. C., Sheu, B. S., Kuo, H.J., & Popper, M. (2008). Helicobacter pylori-related gastric histology classification using support-vector-machine-based feature selection. IEEE Transactions on Information Technology in Biomedicine, 12(4), 523-531.
Hubel, D. H., & Wiesel, T.N. (1968). Receptive fields and functional architecture of monkey striate cortex. The Journal of physiology, 215-243.
International Agency for Research on Cancer. (1994). Helicobacter Pylori. IARC MONOGRAPHS, 100, 385-435.
Itoh, T., Kawahira, H., Nakashima, H., & Yata, N. (2018). Deep learning analyzes Helicobacter pylori infection by upper gastrointestinal endoscopy images. Endoscopy International Open, 6, 139-144.
James, K. Y. H., Lai, W. Y., Ng, W. K., Suen, M. M. Y., Underwood, F. E., Tanyingoh, D., Malfertheiner, P., Graham, D. Y., Wong, V. W. S., Wu, J. C. Y., Chan, F. K. L., Sung, J. J. Y., Kaplan, G. G., & Ng, S. C.(2017). Global Prevalence of Helicobacter pylori Infection: Systematic Review and Meta-Analysis. Gastroenterology, 153, 420-429.
Jiuxiang, G., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., & Chen, T. (2018). Recent advances in convolutional neural networks. Pattern Recognition, 77, 354-377.
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet Classification with Deep Convolutional Neural Networks. NIPS''12 Proceedings of the 25th International Conference on Neural Information Processing Systems, 1, 1097-1105.
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., Jeroen, A.W.M. Laak, V. D., Ginneken, B. N., & Sánchez, C. I. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 60-88.
Maas, A. L., Hannun, A. Y., & Ng, A. Y. (2013). Rectifier nonlinearities improve neural net- work acoustic models. Proceedings of the International Conference on Machine Learning (ICML), 30.
Malfertheine, P., Megraud, F., Morain, C. O., Bazzoli, F., Omar, E. E., Graham, D., Hunt, R., Rokkas, T., Vakil, N., & Kuipers, E. J. (2007). Current concepts in the management of Helicobacter pylori infection: the Maastricht III Consensus Report. Gut, 56(6), 772-781.
Malfliet, W., & Hereman, W. (1996). The Tanh Method: II. Perturbation Technique for Conservative Systems. Physica Scripta, 54, 569-575.
McCulloch, W. S., Warren, S., & Walter, P. (1943). A Logical Calculus of Ideas Immanent in Nervous Activity. Bulletin of Mathematical Biophysics, 5 (4), 115-133.
Min, J. K., Kwak, M. S., & Cha, J. M. (2019). Overview of Deep Learning in Gastrointestinal Endoscopy. Gut and Liver, 13 (4), 388-393.
Nair, V. & Hinton G.E. (2010). Rectified Linear Units Improve Restricted Boltzmann Machines. 27th International Conference on International Conference on Machine Learning (ICML), 807-814.
Nakashima, H., Kawahira, H., Kawachi, H., & Sakaki, N. (2018). Artificial intelligence diagnosis of Helicobacter pylori infection using blue laser imaging-bright and linked color imaging: a single-center prospective study. Annals of Gastroenterology, 31, 462-468.
Oquab, M., Bottou, L., Laptev, I., & Sivic, J. (2014). Learning and transferring mid-level image representations using convolutional neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1717-1724.
Qian N. (1999). On the momentum term in gradient descent learning algorithms. Neural Networks, 12(1), 145-151.
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A, Khosla, A., Bernstein, M., Berg, A. C., Li, F. F. (2015). Imagenet large scale visual recognition challenge. International Journal of Computer Vision (IJCV), 115(3), 211-252.
Salara, A.(2019). Gastric MALT lymphoma and Helicobacter pylori. Medicina Clínica, 152(2), 65-71.
Sharma, N., Jain, V., Mishra, A. (2018). An analysis of convolutional neural networks for image classification. Procedia Computer Science, 132, 377-384.
Shen, Y. (2005). Loss Functions For Binary Classification and Class Probability Estimation. University of Pennsylvania, 1-94.
Shichijo, S., Nomura, S., Aoyama, K., Nishikawa, Y., Miura, M., Shinagawa, T., Takiyama, H., Tanimoto, T., Ishihara, S., Matsuo, K., & Tada, T. (2017). Application of Convolutional Neural Networks in the Diagnosis of Helicobacter pylori Infection Based on Endoscopic Images. EBioMedicine, 25, 106-111.
Shin, H. C., Roth, H. R., Gao, M., Lu, L., Xu, Z., Nogues, I., Yao, J., Mollura, D., & Summers, R.M. (2016). Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Transactions on Medical Imaging, 35(5), 1285-1298.
Simonyan, K. & Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. Proceedings of the International Conference on Learning Representations (ICLR), 1-14.
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1-9.
Szegedy, C., Vanhoucke, V., Ioffe, S., & Shlens, J. (2016). Rethinking the Inception Architecture for Computer Vision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2818-2826.
Wang, C., Yuan, Y., Hunt, R. H. (2007). The association between Helicobacter pylori infection and early gastric cancer: a meta-analysis. The American journal of gastroenterology, 102, 1789-1798.
Watanabe, K., Nagata, N., Shimbo, T., Nakashima, R., Furuhata, E. , Sakurai, T., Akazawa, N., Yokoi, C., Kobayakawa, M., Akiyama, J., Mizokami, M., & Uemura, N. (2013). Accuracy of endoscopic diagnosis of Helicobacter pylori infection according to level of endoscopic experience and the effect of training. BMC Gastroenterology, 13(128), 1-7.
Wijnhoven, R. G. J. & With, P. H. N. D. (2010). Fast training of object detection using stochastic gradient descent. International Conference on Pattern Recognition (ICPR), 2424-2427.
中華民國衛生福利部 (2015)。第三期國家癌症防治計畫( 103-107年)。中華民國: 衛生福利部。
中華民國衛生福利部 (2019)。第四期國家癌症防治計畫( 108-112年)。中華民國: 衛生福利部。
吳登強 (2016)。幽門螺旋桿菌診斷新知。高雄醫師會誌，24(2)，156-160。