- 學位論文
運用微調卷積神經網路診斷肺炎X光影像
Fine-tuning Convolutional Neural Networks for Pneumonia Diagnosis Using X-ray images
摘要
肺炎是世界上造成五歲以下小孩主要死亡原因之一。目前，臨床上透過胸腔 X 光影像進行診斷，由於影像病灶不清楚導致容易被誤診，因此相當仰賴放射科醫師的臨床經驗。由於卷積神經網路與遷移學習方法在電腦輔助診斷上的成功，使得數據集少的醫學影像在檢測疾病中得以有突破。因此，本研究希望可以藉由微調卷積神經網路輔助經驗不足的醫生，或是協助偏遠地區的診所診斷肺炎或是肺炎種類。 在這項研究中，使用著名的圖像分類卷積神經網路 VGG16、VGG19 以及 Inception V3，模型皆使用大數據 ImageNet 預訓練完成。藉由影像中初級特徵的通用性，並接上設計的分類器，從胸腔 X 光影像中檢測肺炎與肺炎種類。最終，三種模型在檢測肺炎的準確率高達98%，並且在 VGG16 中區分細菌性肺炎與病毒性肺炎的準確率達到84%。實驗結果證明先判斷有無肺炎，再區分肺炎種類可以提升診斷的正確性，足夠成為臨床醫師在診斷肺炎時的依據。
並列摘要
Pneumonia is among the top diseases which cause most of the deaths in children all over the world. Nowadays, the disease can be diagnosed from chest X-ray images, but it may be easily misdiagnosed due to unclear imaging lesions. Therefore, it depends on the clinical experience of radiologists. Recently, the success of Convolutional Neural Networks (CNNs) in the application of medical imaging and the emergence of transfer learning, medical imaging with few data sets has made a breakthrough in the detection of diseases. Therefore, the purpose of this study is that computer-aided diagnosis systems can assist inexperienced doctors or clinics in remote areas to diagnose pneumonia or types of pneumonia. In this study, the well-known Convolutional Neural Networks were used, including VGG16, VGG19 and Inception V3. The models were pre-trained through ImageNet. With the versatility of the primary features in the image and the designed classifier, the pneumonia and pneumonia types are detected from the chest X-ray image. In the end, The test results showed that the accuracy of the models in diagnosis pneumonia was 98%, and the accuracy of distinguishing bacterial pneumonia from viral pneumonia in VGG16 achieved 84%. The results prove that judging whether patient suffer pneumonia or not before distinguishing the types of pneumonia can improve the accuracy of pneumonia diagnosis, which is enough to be the basis for clinicians when diagnosing pneumonia.