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研究生:陳美桂
研究生(外文):CHEN, MEI-KUEI
論文名稱:利用田口參數優化之卷積神經網路於 肺癌診斷應用
論文名稱(外文):Using a Convolutional Neural Network with Taguchi Method for Diagnosis Application of Lung Cancer
指導教授:林正堅林正堅引用關係
指導教授(外文):LIN ,CHENG-JIAN
口試委員:潘欣泰郭世崇
口試委員(外文):PAN ,SHING-TAIKUO, SHYE-CHORNG
口試日期:2020-07-10
學位類別:碩士
校院名稱:國立勤益科技大學
系所名稱:資訊工程系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2020
畢業學年度:108
語文別:中文
論文頁數:66
中文關鍵詞:卷積神經網路田口法肺癌電腦斷層掃描參數優化直交表
外文關鍵詞:Convolutional neural networkTaguchi methodlung cancer computed tomographyparameter optimizationorthogonal table
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肺癌是導致癌症死亡的常見原因之一,相較於傳統的胸部X光檢查,使用電腦斷層掃描(Computed Tomography,CT)進行的肺癌篩檢可將肺癌死亡率降低20%。因此,肺部CT圖像檢測在全世界廣泛使用。近年來,深度學習中的卷積神經網路圖像處理上展現出比傳統辨識方法更加優異的效能。卷積神經網路將影像以數百萬的神經網路參數向輸出端傳遞,於輸出端計算目標與預測的誤差,達成具高變化且高維的影像辨識。因而有越來越多研究嘗試以不同的深度學習技術,希望提高肺癌影像檢測準確率。
因此本研究提出以田口法參數設計優化卷積神經網路(Taguchi-based Convolutional neural network,T-CNN),對CT圖像進行肺癌辨識分類。在田口法中,以8個控制因子3個水準選擇了L36進行實驗來確定T-CNN的最佳參數。本研究以兩個肺癌數據集分別為(A) TCIA(LIDC-IDRI)和(B) TCIA (SPIE-AAPM Lung CT Challenge)進行實驗。實驗結果證明採用田口參數優化的T-CNN和原始CNN在肺癌辨識的平均分類準確率,數據集(A) 分別為98.83%和91.97%;數據集(B)分別為99.97%和94.68%,比原始的CNN分別提高了6.86%和5.29%,證實本研究提出一個更高的準確度模型,並提高了肺癌的分類準確性。

Lung cancer is one of the common causes of cancer death. Compared with traditional chest X-ray examination, lung cancer screening using Computed Tomography (CT) can reduce lung cancer mortality by 20%. Therefore, lung CT image detection is widely used all over the world. In recent years, the Convolutional Neural Network (CNN) in deep learning (DNN) has shown more excellent performance in image processing than traditional identification methods. The convolutional neural network transfers the image to the output with millions of neural network parameters, and calculates the error between the target and the prediction at the output to achieve high-variation and high-dimensional image recognition.
Therefore, this study proposes to use the Taguchi method to design and optimize the Taguchi-based Convolutional neural network (T-CNN) to classify the CT images for lung cancer identification. In the Taguchi method, L36 was selected with 8 control factors and 3 levels to conduct experiments to determine the optimal parameters of T-CNN. In this study, the two lung cancer data sets were (A) TCIA (LIDC-IDRI) and (B) TCIA SPIE-AAPM Lung CT Challenge. The experimental results prove that the average classification accuracy of T-CNN and original CNN optimized by Taguchi parameters in lung cancer identification is 98.83% and 91.97% in data set (A); 99.97% and 94.67% respectively in data set (B) The original CNN was improved by 6.86% and 5.29% respectively, confirming that this study proposed a higher accuracy model and improved the classification accuracy of lung cancer.

摘要 I
ABSTRACT III
誌謝 V
目錄 VI
表目錄 VIII
圖目錄 X
第一章 緒論 1
1.1研究動機與背景 1
1.2研究目的 4
1.3 論文架構 5
第二章 文獻探討 7
2.1 卷積神經網路 7
2.2 肺部疾病相關研究 11
2.3 田口法 (Taguchi method)相關研究 12
第三章 研究方法 14
3.1田口法 15
3.2資料集來源 20
3.3卷積神經網路架構 27
3.4進行模型評估驗證 34
第四章 實驗結果 35
4.1 LIDC-IDRI資料集實驗結果 36
4.2 SPIE-AAPM資料集實驗結果 44
4.3 實驗結果分析 49
第五章 結論 52
5.1 結論 52
5.2 未來發展 54
參考文獻 55


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