現代醫療影像中，胸腔X光影像為臨床使用上，能夠快速取得病患胸腔內部資訊的醫療影像，並可經由此影像來對病人做初步診斷。本研究以人工智慧的方式，訓練模型來自動判別該病患是否為肺部渾濁。然而人工智慧也有其受限之處，在臨床上可能會遇到資料不足的狀況，這樣的情況下可能會造成訓練的模型預測結果不佳，因此本研究使用不同訓練方法，讓模型可以在資料受限的情形下也能有一定判別效果，且探討不同網路架構訓練出的模型準確度差異，提出較好方案。此外醫生在診斷時，會根據病患年齡性別、過去診斷紀錄或是其它檢查結果來同時判讀，因此使用特徵結合法來模擬此情境，了解在擁有多種資訊時，模型預測結果的差異。本研究最後提出以機器學習模型預測結果回推計算的方式，產生出模型對於影像關注區域圖的熱力圖。

In the applications of medical imaging, chest x-ray is a useful tool for the diagnosis of lung diseases. In this study, we implemented a classification system to automatically determine if the patient has lung opacities. We studied various classification models based on machine and deep learning and evaluated the performance of these models to identify a suitable model for datasets with limited sample sizes and applied it on the pneumonia datasets. We also investigated on how to combine various features obtained from chest x-ray. Finally, this study proposes a method to interpret results by generating heat-maps to locate the chest regions that could be associated with the lung opacities.

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