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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 傅立成 | zh_TW |
dc.contributor.advisor | Li-Chen Fu | en |
dc.contributor.author | 林恩廷 | zh_TW |
dc.contributor.author | En-ting Lin | en |
dc.date.accessioned | 2023-10-03T16:23:41Z | - |
dc.date.available | 2023-11-09 | - |
dc.date.copyright | 2023-10-03 | - |
dc.date.issued | 2023 | - |
dc.date.submitted | 2023-08-08 | - |
dc.identifier.citation | Giovanni Dimauro, Maria Elena Griseta, Mauro Giuseppe Camporeale, Felice Clemente, Attilio Guarini, and Rosalia Maglietta. An intelligent non-invasive system for automated diagnosis of anemia exploiting a novel dataset. Artificial Intelligence in Medicine, 136:102477, 2023.
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778, 2016. Yong Li, Jiabei Zeng, Jie Zhang, Anbo Dai, Meina Kan, Shiguang Shan, and Xilin Chen. Kinnet: Fine-to-coarse deep metric learning for kinship verification. In Proceedings of the 2017 workshop on recognizing families in the wild, pages 13–20, 2017. Bruno De Benoist, Mary Cogswell, Ines Egli, and Erin McLean. Worldwide prevalence of anaemia 1993-2005; who global database of anaemia. 2008. Erin McLean, Mary Cogswell, Ines Egli, Daniel Wojdyla, and Bruno De Benoist. Worldwide prevalence of anaemia, who vitamin and mineral nutrition information system, 1993–2005. Public health nutrition, 12(4):444–454, 2009. Kushang V Patel. Epidemiology of anemia in older adults. In Seminars in hematology, volume 45, pages 210–217. Elsevier, 2008. Rebecca J Stoltzfus, Anbarasi Edward-Raj, Michele L Dreyfuss, Marco Albonico, Antonio Montresor, Makar Dhoj Thapa, Keith P West Jr, Hababuu M Chwaya, Lorenzo Savioli, and James Tielsch. Clinical pallor is useful to detect severe anemia in populations where anemia is prevalent and severe. The Journal of nutrition, 129(9):1675–1681, 1999. Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár. Focal loss for dense object detection. In Proceedings of the IEEE international conference on computer vision, pages 2980–2988, 2017. Selim Suner, Gregory Crawford, John McMurdy, and Gregory Jay. Non-invasive determination of hemoglobin by digital photography of palpebral conjunctiva. The Journal of emergency medicine, 33(2):105–111, 2007. Azwad Tamir, Chowdhury S Jahan, Mohammad S Saif, Sums U Zaman, Md Mazharul Islam, Asir Intisar Khan, Shaikh Anowarul Fattah, and Celia Shahnaz. Detection of anemia from image of the anterior conjunctiva of the eye by image processing and thresholding. In 2017 IEEE Region 10 Humanitarian Technology Conference (R10-HTC), pages 697–701. IEEE, 2017. Md Imran Khan, Raktim Kumar Mondol, Muhammad Ahsan Zamee, and Tanvir Ahmad Tarique. Hardware architecture design of anemia detecting regression model based on fpga. In 2014 International Conference on Informatics, Electronics & Vision (ICIEV), pages 1–5. IEEE, 2014. Shaun Collings, Oliver Thompson, Evan Hirst, Louise Goossens, Anup George, and Robert Weinkove. Non-invasive detection of anaemia using digital photographs of the conjunctiva. PloS one, 11(4):e0153286, 2016. Yi-Ming Chen and Shaou-Gang Miaou. A kalman filtering and nonlinear penalty regression approach for noninvasive anemia detection with palpebral conjunctiva images. Journal of healthcare engineering, 2017, 2017. MD Anggraeni and A Fatoni. Non-invasive self-care anemia detection during pregnancy using a smartphone camera. In IOP Conference Series: Materials Science and Engineering, volume 172, page 012030. IOP Publishing, 2017. Peter Appiahene, Justice Williams Asare, Emmanuel Timmy Donkoh, Giovanni Dimauro, and Rosalia Maglietta. Detection of iron deficiency anemia by medical images: a comparative study of machine learning algorithms. BioData Mining, 16(1):1–20, 2023. Justice Williams Asare, Peter Appiahene, Emmanuel Timmy Donkoh, and Giovanni Dimauro. Iron deficiency anemia detection using machine learning models: A comparative study of fingernails, palm and conjunctiva of the eye images. Engineering Reports, page e12667, 2023. Edward Jay Wang, William Li, Doug Hawkins, Terry Gernsheimer, Colette NorbySlycord, and Shwetak N Patel. Hemaapp: noninvasive blood screening of hemoglobin using smartphone cameras. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pages 593–604, 2016. Robert G Mannino, David R Myers, Erika A Tyburski, Christina Caruso, Jeanne Boudreaux, Traci Leong, GD Clifford, and Wilbur A Lam. Smartphone app for non-invasive detection of anemia using only patient-sourced photos. Nature communications, 9(1):4924, 2018. Aixian Zhang, Jingjiao Lou, Zijie Pan, Jiaqi Luo, Xiaomeng Zhang, Han Zhang, Jianpeng Li, Lili Wang, Xiang Cui, Bing Ji, et al. Prediction of anemia using facial images and deep learning technology in the emergency department. Frontiers in Public Health, 10:3917, 2022. Lijuan Zheng, Shaopeng Liu, Senping Tian, Jianhua Guo, Xinpeng Wang, Xiuxiu Liao, and Jiaming Hong. Enhancing intelligent anemia detection via unifying global and local views of conjunctiva image with two-branch neural networks. 2022. Sohini Roychowdhury, Paul Hage, and Joseph Vasquez. Azure-based smart monitoring system for anemia-like pallor. Future Internet, 9(3):39, 2017. Abhishek Kesarwani, Sunanda Das, Mamata Dalui, Dakshina Ranjan Kisku, Bibhash Sen, Suchismita Roy, and Anupam Basu. Non-invasive anaemia detection by examining palm pallor: A smartphone-based approach. Biomedical Signal Processing and Control, 79:104045, 2023. Vinod Nair and Geoffrey E Hinton. Rectified linear units improve restricted boltzmann machines. In Proceedings of the 27th international conference on machine learning (ICML-10), pages 807–814, 2010. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017. Marcel E Salive, Joan Cornoni-Huntley, Jack M Guralnik, Caroline L Phillips, Robert B Wallace, Adrian M Ostfeld, and Harvey J Cohen. Anemia and hemoglobin levels in older persons: relationship with age, gender, and health status. Journal of the American Geriatrics Society, 40(5):489–496, 1992. Sivachandar Kasiviswanathan, Thulasi Bai Vijayan, Lorenzo Simone, and Giovanni Dimauro. Semantic segmentation of conjunctiva region for non-invasive anemia detection applications. Electronics, 9(8):1309, 2020. Sanghyun Woo, Jongchan Park, Joon-Young Lee, and In So Kweon. Cbam: Convolutional block attention module. In Proceedings of the European conference on computer vision (ECCV), pages 3–19, 2018. Meng-Hao Guo, Zheng-Ning Liu, Tai-Jiang Mu, and Shi-Min Hu. Beyond self-attention: External attention using two linear layers for visual tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022. Nitesh V Chawla, Kevin W Bowyer, Lawrence O Hall, and W Philip Kegelmeyer. Smote: synthetic minority over-sampling technique. Journal of artificial intelligence research, 16:321–357, 2002. Erico Tjoa and Cuntai Guan. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems, 32(11):4793–4813, 2020. Amitojdeep Singh, Sourya Sengupta, and Vasudevan Lakshminarayanan. Explainable deep learning models in medical image analysis. Journal of Imaging, 6(6):52, 2020. Ran Gu, Guotai Wang, Tao Song, Rui Huang, Michael Aertsen, Jan Deprest, Sébastien Ourselin, Tom Vercauteren, and Shaoting Zhang. Ca-net: Comprehensive attention convolutional neural networks for explainable medical image segmenta- tion. IEEE transactions on medical imaging, 40(2):699–711, 2020. Giovanni Dimauro, Serena De Ruvo, Federica Di Terlizzi, Angelo Ruggieri, Vincenzo Volpe, Lucio Colizzi, and Francesco Girardi. Estimate of anemia with new non-invasive systems—a moment of reflection. Electronics, 9(5):780, 2020. | - |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/90506 | - |
dc.description.abstract | 貧血一直都是一項全球性的健康議題,根據世界衛生組織(WHO)統計,全球有超過十億人在不同程度上受到貧血問題的影響,就臨床的觀點而言,急性貧血通常是由於出血所造成,嚴重時甚至會危及病人的性命,因此在本研究中,我們著重在對病患血紅素濃度的預測,期望藉由快速且精確的自動化流程來輔助醫師在臨床的診斷。
貧血診斷的黃金標準來自血液中血紅素濃度的實驗室測量,必須通過抽血流程來取得,臨床上為求處置的即時性,醫生經常會透過檢查病人的眼結膜等部位是否蒼白來判斷貧血,然而此方法需要醫生的經驗輔助且具一定的主觀性,因此我們著眼於透過電腦視覺的方法,建立基於眼結膜、舌頭、手掌、甲床四個患部影像輸入的深度學習模型。由於四個患部的影像特徵並不一致,例如在眼結膜影像中微血管的特徵可以提供更多資訊,在其他三個部位卻不容易觀測到此特徵,因此本研究提出了一種新的預測方式,透過輸入額外的患部標籤,搭配融合注意力機制,讓模型在訓練過程中能夠自行學習並強化各個患部的重點特徵,藉以產生足以信賴的結果。與此同時,為了解決資料集中所遇到的資料不平衡問題,我們引入對偶損失函數,讓回歸模型得已受益於廣為使用的分類方法,進而達到穩定處理少數樣本的目的。 總結來說,我們建立了一套基於影像輸入的非侵入式血紅素預測模型,並期望能以現場AI輔助系統的方式為臨床帶來幫助。 | zh_TW |
dc.description.abstract | Anemia is a significant global health issue, affecting over a billion people worldwide to varying degrees, according to the World Health Organization (WHO). Acute anemia is typically caused by bleeding and can be life-threatening in severe cases. Therefore, this study focuses on the detection of different situations of hemoglobin concentration in patients, intending to assist clinical diagnosis through a rapid and accurate automatic process. Generally, the gold standard for diagnosing anemia relies on laboratory measurements of hemoglobin concentration in the blood, which requires a blood drawing. To meet the need for real-time intervention in clinical practice, physicians often rely on visual examination of specific areas, such as the conjunctiva, to assess pallor and infer anemia. However, this method is subjective and relies on the physician's experience. Therefore, we turn to computer vision techniques and propose a deep learning prediction model based on four input images from different body parts, namely, conjunctiva, tongue, palm, and fingernail. Given that the image features vary across the four body parts, our approach is considered highly novel. By incorporating additional body part labels and employing a fusion attention mechanism, the model learns and enhances the salient features of each body part during training, enabling it to produce reliable results. Furthermore, to address the issue of data imbalance in the dataset, we employ a dual loss function that allows the regression model to benefit from well-established classification methods, thereby achieving stable handling of minority samples.
To sum up, we have developed a non-invasive hemoglobin prediction model based on image input, with the goal of supporting clinical practice through an AI-based on-site system. Such results have been verified by real experiment done in National Taiwan University Hospital involving 59 patient subjects, and the prediction accuracy as well as F1-score can achieve as high as 0.658 and 0.778, respectively. | en |
dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-10-03T16:23:41Z No. of bitstreams: 0 | en |
dc.description.provenance | Made available in DSpace on 2023-10-03T16:23:41Z (GMT). No. of bitstreams: 0 | en |
dc.description.tableofcontents | 致謝 i
中文摘要 ii ABSTRACT iii CONTENTS v LIST OF FIGURES viii LIST OF TABLES x Chapter 1 Introduction 1 1.1 Background 1 1.2 Motivation 3 1.3 Objectives 5 1.4 Related Work 6 1.4.1 Non-invasive Anemia Detection 6 1.4.2 Machine-Learning-Based Methods 7 1.4.3 Deep-Learning-Based Methods 8 1.5 Contributions 8 1.6 Thesis Organization 9 Chapter 2 Preliminaries 11 2.1 Convolutional Neural Network 11 2.1.1 Convolutional Layers 12 2.1.2 Pooling Layers 13 2.1.3 Fully Connected Layer 14 2.1.4 Activation Function 15 2.1.5 Residual Network 16 2.2 Attention Mechanism 17 Chapter 3 Methodology 20 3.1 System Overview 20 3.2 Image Preprocessing 22 3.3 Channel-Spatial Attention Module 23 3.4 Body Part Tag 26 3.5 Fusion Attention Module 27 3.6 Dual Loss 29 Chapter 4 Experiments 32 4.1 Datasets 32 4.1.1 EYES-DEFY-ANEMIA Dataset 32 4.1.2 NTUH dataset 34 4.2 Evaluation Metrics 36 4.3 Implementation Details 38 4.4 Ablation Study 39 4.4.1 Results on EYES-DEFY-ANEMIA Dataset 40 4.4.2 Results on NTUH Dataset 41 4.5 Experimental Result on EYES-DEFY-ANEMIA Dataset 43 4.5.1 Quantitative Results 43 4.5.2 Qualitative Results 45 4.5.3 Experimental Analysis of Dual Loss Module 46 4.6 Experimental Reslt on NTUH Dataset 47 4.6.1 Quantitative Results 47 Chapter 5 Conclusion 51 REFERENCES 53 | - |
dc.language.iso | en | - |
dc.title | 基於不同患部影像輸入之深度學習模型應用於非侵入式血紅素濃度偵測 | zh_TW |
dc.title | Deep Learning-Based Model for Non-invasive Hemoglobin Estimation via Body Parts Images | en |
dc.type | Thesis | - |
dc.date.schoolyear | 111-2 | - |
dc.description.degree | 碩士 | - |
dc.contributor.oralexamcommittee | 黃建華;蔡居霖;李明穗;陳祝嵩 | zh_TW |
dc.contributor.oralexamcommittee | Chien-Hua Huang;Chu-Lin Tsai;Ming-Sui Lee;Chu-Song Chen | en |
dc.subject.keyword | 血紅素濃度,貧血偵測,深度學習,電腦視覺, | zh_TW |
dc.subject.keyword | Hemoglobin Estimation,Anemia Detection,Deep Learning,Computer Vision, | en |
dc.relation.page | 57 | - |
dc.identifier.doi | 10.6342/NTU202303357 | - |
dc.rights.note | 同意授權(限校園內公開) | - |
dc.date.accepted | 2023-08-10 | - |
dc.contributor.author-college | 電機資訊學院 | - |
dc.contributor.author-dept | 資訊工程學系 | - |
顯示於系所單位: | 資訊工程學系 |
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