基於YOLOv4在超音波影像中的甲狀腺腫瘤識別與偵測

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蔡承宏(Cheng-Hung Tsai) 查詢紙本館藏

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電機工程學系

論文名稱

基於YOLOv4在超音波影像中的甲狀腺腫瘤識別與偵測
(Thyroid Tumor Identification and Detection in Ultrasound Images by YOLOv4)

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至系統瀏覽論文 (2027-2-1以後開放)

摘要(中)

甲狀腺腫瘤是內分泌系統常見的病症。甲狀腺腫瘤的危害不大，不影響日常生活。不過當甲狀腺腫瘤被診斷為惡性腫瘤，或是腫瘤過大壓迫到氣管，就需要即時手術治療。通常使用超音波檢測甲狀腺腫瘤，這是一個簡單、快速和省錢的方法，也是非侵入性和無輻射的檢測方法。不過為了近一步的診斷甲狀腺腫瘤的良惡性，還是需要使用細微針頭提取腫瘤細胞，再用顯微鏡診斷腫瘤類型。目前判讀超音波影像中的甲狀腺腫瘤，還是依賴於臨床醫師的肉眼，這種判定方式不僅耗時且會因經驗而影響判讀。
本研究提出一個基於YOLO v4深度神經網路開發一個自動檢測超音波甲狀腺腫瘤之檢測器，實現快速偵測甲狀腺腫瘤。我們選擇七個預訓練的卷積神經網路作為特徵提取網路，結合YOLO v4網路，創建了七個自定義的YOLO v4檢測器。我們使用影像修復法移除了超音波影像中的記號，然後使用數據增量增加影像數量和提高影像對比度。經過5-fold交叉驗證後，當特徵提取網路是NASNet-Large時，模型在測試集的評估指標達到:平均Precision為92.2%、平均Recall為 85.7%、平均F1-score為 88.8%和平均Average Precision為 84%。最後我們設計一個圖形使用者介面，輔助臨床醫師方便快速診斷超音波影像上的甲狀腺腫瘤。

摘要(英)

The thyroid tumor is a common condition in the endocrine system. Thyroid tumors typically pose minimal risk and do not significantly impact daily life. However, when a thyroid tumors is diagnosed as a malignant tumor or when the tumor is excessively large and compressing the trachea, immediate surgical treatment is necessary. Thyroid tumors are typically detected using ultrasound, a simple, quick, cost-effective, non-invasive, and radiation-free method of examination. However, for a more detailed assessment of the benign or malignant nature of thyroid nodules, fine-needle aspiration (FNA) is required to extract cells from the tumor for microscopic examination to determine the type of tumor. Currently, the interpretation of thyroid nodules in ultrasound images still relies on the visual assessment of clinical physicians. This method is not only time-consuming but also susceptible to interpretation variations based on experience, affecting the accuracy of diagnosis.
This study proposes the development of an automatic thyroid nodule detection system based on the YOLO v4 deep neural network. This system aims to achieve rapid detection of thyroid nodules in ultrasound images. We selected seven pre-trained convolutional neural networks as feature extraction networks and combined them with the YOLO v4 network, creating seven customized YOLO v4 detectors. We utilized image inpainting techniques to remove artifacts from the ultrasound images and employed data augmentation to increase the image quantity and enhance image contrast. After 5-fold cross-validation, when the feature extraction network was NASNet-Large, the model achieved the following evaluation metrics on the test set: average Precision = 92.2%, average Recall = 85.7%, average F1-score = 88.8%, and average AP = 84%. We concluded by designing a graphical user interface to assist clinical physicians in efficiently diagnosing thyroid nodules in ultrasound images, providing them with convenience and speed during the diagnostic process.

關鍵字(中)

★ 甲狀腺腫瘤
★ 超音波影像
★ 影像修復
★ YOLOV4
★ 卷積神經網路
★ 深度學習

關鍵字(英)

★ thyroid tumor
★ ultrasound imaging
★ image inpainting
★ YOLOV4
★ convolutional neural network
★ deep learning

論文目次

摘要 v
Abstract vi
目錄 viii
圖目錄 x
表目錄 xii
第一章緒論 1
1.1 研究動機 1
1.2 甲狀腺癌分類 3
1.3 相關研究 4
第二章研究方法 7
2.1 資料集 8
2.2 影像前處理 10
2.2.1 影像修復 10
2.2.2 影像數據增量 15
2.3 卷積神經網路 16
2.4 預訓練卷積神經網絡 20
2.5 物件檢測 21
2.6 YOLOv4 22
2.7 資料集處裡 24
2.8 交叉驗證 25
2.9 評估指標 26
第三章研究結果 31
3.1 YOLO v4檢測結果 31
3.1.1 沒有使用數據增量的訓練結果 31
3.1.2 使用數據增量(水平翻轉)的訓練結果 34
3.1.3 使用數據增量(增強影像對比度)的訓練結果 37
3.1.4 使用數據增量(水平翻轉+增強影像對比度)的訓練結果 41
3.2外部測試集評估 44
第四章討論 48
4.1 影像修復的侷限性 48
4.2 腫瘤分類模型 48
4.3 IoU和評估指標的關係 50
4.4 與現有方法之比較 56
4.5 GUI設定 56
4.6未來的工作 57
第五章結論 58
參考文獻 59
附錄 63

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指導教授

蔡章仁彭徐鈞(Jang-Zern Tsai Syu-Jyun Peng)

審核日期

2024-1-26

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