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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 張瑞峰(Ruey-Feng Zhang) | |
| dc.contributor.author | Yi-Sin Liu | en |
| dc.contributor.author | 劉易鑫 | zh_TW |
| dc.date.accessioned | 2022-11-24T09:26:59Z | - |
| dc.date.available | 2022-11-24T09:26:59Z | - |
| dc.date.copyright | 2021-11-06 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-10-13 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/81764 | - |
| dc.description.abstract | " 壓瘡是一種發生率高的傷病，雖然致死機會不大但治療的代價高昂。為了給予壓瘡適當的治療，國際壓瘡諮詢委員會(National Pressure Ulcer Advisory Panel, NPUAP)將傷口依照侵入皮膚的嚴重度分從第一級至第四級。然而，不同傷口範圍及深度會影響判斷，甚至有出現診斷錯誤的可能性。因此需要一個電腦輔助診斷系統(Computer-aided Diagnosis System)協助醫護人員進行壓瘡的診斷。近年，卷積神經網路(CNN)被廣泛地用於診斷壓瘡。藉由專注在區域特徵，區域基底卷積神經網路(Region-based CNN, R-CNN)架構更是對偵測且診斷壓瘡具有優異的能力。由於區域基底卷積神積網路(R-CNN)架構的表現依賴於有效的特徵抓取，所以一個強力的特徵抓取模型為增強偵測及診斷壓瘡是需要的。 因此，為了設計一套電腦輔助診斷系統以進行壓瘡偵測及診斷，在這篇研究中我們使用了級聯區域基底卷積神經網路(Cascade R-CNN)與壓縮激勵移動窗口變換器(Squeeze-and-excitation Shifted Windows Transformer, SE-Swin Transformer)模型。此套電腦輔助診斷系統由資料擴增、特徵提取和傷口評級三部分組成。在資料擴增的部分我們調整了影像尺寸並增加影像，而後將經過擴增的影像送進我們提出的壓縮激勵移動窗口變換器(SE-Swin Transformer)做特徵提取。經過特徵擷取後，具階層式的特徵會被送進傷口評級產生出壓瘡偵測及診斷之結果。在傷口評級中，由於級聯區域基底卷積神經網路(Cascade R-CNN)可以產生一階階精準的偵測，因此將其作為我們的評級模型，最後產生壓瘡偵測與診斷之結果。此研究總共使用了883張壓瘡影像，其中包含240個第一級傷口，226個第二級傷口，203個第三級傷口和214個第四級傷口。根據實驗結果顯示，我們提出的系統能在偵測表現上達到平均精度均值81.3%，也能在診斷表現上達到準確率87.1%、靈敏度85.7%、陽性預測值86.6%的結果，顯示出我們提出的電腦輔助系統具良好的偵測與診斷能力。" | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-24T09:26:59Z (GMT). No. of bitstreams: 1 U0001-0810202119154900.pdf: 1247640 bytes, checksum: 0f29b1028faa52938f6db5af09544d42 (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | Table of Contents 口試委員會審定書 I 致謝 II 摘要 III Abstract V Table of Contents VII List of Figures IX List of Tables X Chapter 1. Introduction 1 Chapter 2. Materials 5 2.1 Data Collection 5 2.2 Pressure Injury Categorization 5 Chapter 3. Methods 6 3.1 Data Augmentation 8 3.2 Feature Extraction 8 3.2.1 Shifted windows Transformer (Swin Transformer) 9 3.2.2 Squeeze-and-Excitation (SE) block 12 3.3 Injury Grading 14 3.4 Loss Function 16 Chapter 4. Results and Discussion 18 4.1 Experiment Environment 18 4.2 Evaluation 18 4.2.1 Injury Detection Results 19 4.2.2 Injury Classification Results 21 4.3 Discussion 26 Chapter 5. Conclusion and Future Work 30 Reference 32 | |
| dc.language.iso | en | |
| dc.subject | 區域基底卷基神經網路 | zh_TW |
| dc.subject | 壓瘡 | zh_TW |
| dc.subject | 電腦輔助診斷 | zh_TW |
| dc.subject | 注意力機制 | zh_TW |
| dc.subject | 變換器 | zh_TW |
| dc.subject | attention mechanism | en |
| dc.subject | Pressure injury | en |
| dc.subject | region-based convolutional neural network | en |
| dc.subject | computer-aided diagnosis | en |
| dc.subject | transformer | en |
| dc.title | 利用移動窗口變換器級聯區域基底卷積神經網路於壓瘡偵測與診斷 | zh_TW |
| dc.title | Detection and Diagnosis for Pressure Injury by Using Swin-Cascade R-CNN | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 羅崇銘(Hsin-Tsai Liu),陳啟禎(Chih-Yang Tseng) | |
| dc.subject.keyword | 壓瘡,電腦輔助診斷,注意力機制,變換器,區域基底卷基神經網路, | zh_TW |
| dc.subject.keyword | Pressure injury,computer-aided diagnosis,attention mechanism,transformer,region-based convolutional neural network, | en |
| dc.relation.page | 36 | |
| dc.identifier.doi | 10.6342/NTU202103625 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2021-10-13 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 生醫電子與資訊學研究所 | zh_TW |
| 顯示於系所單位： | 生醫電子與資訊學研究所 | |
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