肺炎指的是肺部出現發炎的症狀主要會影響肺泡功能，成因通常為受到細菌或病毒感染，肺炎常見的症狀包含咳嗽、咳痰、胸痛、呼吸困難、發燒等，透過胸前X光影像可以呈現其病灶區域與檢測其病理狀況，本研究的目的在於建立針對胸前X光影像的全自動診斷並標記肺炎區域之系統，透過此系統之分析及判斷，檢測X光影像中肺炎之病灶區域。在這項研究中，我們使用二維卷積神經網路及胸前X光影像來進行肺炎區域的標記和辨別。我們所使用的資料為2018 RSNA（北美放射科協會）肺炎偵測挑戰賽所提供之胸前X光影像訓練集，除了比較不同網路架構的神經網路表現結果之外，同時也研究出一種訓練模型之方式稱為「批次資料控制」，探討在固定批次資料量大小的條件下，透過調整批次訓練資料內不同類別之資料比例，並比較各種的資料比例組合作為卷積神經網路輸入資料所獲得之結果，進而發現透過「批次資料控制」，有助於控制模型之靈敏度、特異度等表現，且使用「批次資料控制」之模型表現也較穩定。透過此種模型訓練方式能解決醫療影像中常出現的資料類別不平衡之問題。

In this study, we implemented an automatic segmentation system for detecting and locating lung opacities of pneumonia from frontal chest x-ray radiographs. We used frontal chest x-ray images as the input data of the convolution neural network to conduct the segmentation of pneumonia lesions and compare the performance of three different neural networks, mostly used for segmentation application. Furthermore, we excogitated a training method called “batch controlling” to solve the problem of data type unbalanced, which usually happened in medical image training. As its name implied, “batch controlling” means to control the data type combination in each batch, whose size was fixed, in our training process. The results supported that “batch controlling” could make models more stable and be conducive to adapt the performances of models, such as sensitivities and specificities. The batch controlling method could be advantageous to the convolution neural network with class-unbalanced datasets of medical imaging applications.

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