臺灣博碩士論文加值系統

目錄
摘要 i
Abstract ii
誌謝 iii
表目錄 vii
圖目錄 viii
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 5
1.3 論文架構 7
第二章 文獻探討與背景知識 8
2.1 國內外研究發展 8
2.1.1 基於指甲甲床圖像之非侵入式貧血檢測 8
2.1.2 Mobile-PTAS (m-PTAS)便攜式血紅蛋白檢測器 9
2.1.3 AcneDet人工智慧系統 10
2.1.4 文獻探討與回顧 12
2.2 行動醫療應用程式 13
2.3 手機端應用程式全端開發技術 14
2.3.1 前端開發技術 14
2.3.2 後端開發技術 15
2.3.3 應用程式開發介面-API 15
2.4 軟體開發套件Flutter 18
2.5 雲端運算服務 19
2.5.1 AWS EC2 21
2.5.2 Google Firebase 22
第三章 研究方法 23
3.1 系統架構介紹 23
3.1.1 病徵檢測版本 24
3.1.2 臉部病徵檢測流程 25
3.1.3 用戶端-Flutter框架 27
3.1.4 伺服器端 - Firebase雲端平台 28
3.1.4.1 Firebase Authentication 28
3.1.4.2 Cloud Firestore 29
3.1.4.3 Firebase Storage 30
3.1.5 伺服器端-模型運行平台 30
3.2 伺服器端研製 34
3.2.1 Firebase建置 34
3.2.2 Cloud Firestore建置 35
3.2.3 Firebase Storage建置 36
3.2.4 AWS EC2 虛擬主機建置 37
3.2.5 REST API建置 38
3.2.6 雲端模式病徵檢測 39
3.2.7 伺服器端模型載入 40
3.2.8 伺服器端模型分析 41
3.3 行動端應用程式研製 42
3.3.1 選擇視窗 43
3.3.2 全臉拍攝相機 45
3.3.3 Pytorch Mobile 48
3.3.4 Tensorflow Lite 49
3.3.5 本地端模型載入 49
3.3.6 XNNPack Delegate / GPU Delegate 53
3.3.7 本地緩存功能 54
第四章 研製成果 57
4.1 身分驗證系統 57
4.2 選擇視窗 58
4.3 圖像剪裁功能 61
4.4 全臉拍攝功能 62
4.5 臉部病徵檢測 64
4.5.1 貧血檢測 64
4.5.2 痤瘡檢測 67
第五章 結論與未來發展 70
5.1 結論 70
5.2 未來發展 71
參考文獻 72

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