醫療檢查項目有許多需要專業人士進行判讀，但往往需要醫療人士的經驗去做判斷，需要大量的時間與成本來培養專業人士，特別是涉及免疫風濕科檢查的自身免疫疾病，常需要專業人士進行抗核抗體的判讀。這是因為抗核抗體檢測是診斷許多免疫相關疾病的重要指標，如系統性紅斑狼瘡（SLE）、類風濕性關節炎等。需要專業人員在螢光顯微鏡下觀察、判讀和評估抗核抗體的螢光模式，較為耗時且容易受到主觀因素影響判斷。

故本篇提出以卷積神經網路的細胞辨識模型來協助醫療人員能夠更快速、準確地辨別細胞，提高診斷的準確性和效率。
嘗試了五種不同的卷積神經網路模型架構，包括AlexNet、GoogLeNet、VGG16、VGG19和ResNet等，並在這些模型中分析比較。透過大量的實驗，我們得出了使用VGG19模型在判別抗核抗體細胞上有最佳效果的結論，其正確率達到98.72%。這意味著使用VGG19模型可以快速且準確地辨別抗核抗體細胞，為醫療人員提供了一個有效的輔助工具，幫助他們更好地判斷疾病，提高診斷的準確性和效率。並採用不需要另外學習如何操作使用的LINE BOT進行模型套用，讓醫療人員可以快速上手使用該訓練模型來輔助判別。
總結而言，本研究提出了一種新的方法，即使用卷積神經網路來協助醫療人員更快速、準確地辨別細胞，提高診斷的準確性和效率。使用VGG19模型在判別抗核抗體細胞上取得了最佳效果，並通過LINE BOT使模型應用更加便捷。這項研究的結果將對醫學檢驗領域產生重要的影響，希望能夠為自動化醫學診斷提供一個可行的解決方案。

Medical examinations often require expert interpretation, which can be time-consuming and costly to train. In the field of rheumatology, the interpretation of antinuclear antibodies is critical for diagnosing autoimmune diseases like systemic lupus erythematosus and rheumatoid arthritis.
　　Traditionally, experts observe, interpret, and evaluate antinuclear antibody patterns under a fluorescence microscope, a process prone to subjectivity. To address this, we propose a convolutional neural network (CNN) cell recognition model to assist professionals in accurately identifying cells, improving diagnostic efficiency.
　　We tested five CNN architectures (AlexNet, GoogLeNet, VGG16, VGG19, and ResNet), finding VGG19 achieved the highest accuracy (98.72%) in identifying antinuclear antibody cells. This model offers a valuable tool for medical professionals, enhancing disease diagnosis.Using a LINE BOT, the trained model becomes easily applicable without the need for complex operations, streamlining the process for medical professionals.
　　In conclusion, our CNN-based approach improves cell identification and diagnosis, particularly in autoimmune diseases. VGG19 proves to be the most effective model, with potential applications in automated medical diagnostics, advancing healthcare practices.

第一章 緒論 1
1-1 研究背景 1
1-2 研究動機與目的 3
1-3 研究架構 4
第二章 文獻探討 5
2-1 相關文獻 5
2-2 模型架構 5
2-2.1 AlexNet 7
2-2.2 GoogLeNet 8
2-2.3 VGG16 10
2-2.4 VGG19 12
2-2.5 ResNet 13
2-3 評估方式 15
2-3.1 混淆矩陣 15
第三章 研究方法 17
3-1 資料集 17
3-1.1 資料集 17
3-1.2 資料預處理 17
3-2 模型訓練 19
3-2.1 AlexNet 19
3-2.2 GoogLeNet 20
3-2.3 VGG16 21
3-2.4 VGG19 22
3-2.5 ResNet 23
第四章 實驗與結果討論 25
4-1 細胞辨識結果 25
4-1.1 AlexNet 26
4-1.2 GoogLeNet 27
4-1.3 VGG16 28
4-1.4 VGG19 29
4-1.5 ResNet 30
4-2 辨識系統整合 31
4-2.1 實驗結果討論 31
4-2.1 LINE BOT架設實用 34
第五章 結論與未來展望 37
5-1 結論 37
5-2 未來展望 37
第六章 參考文獻 39

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