Application Of Faster-RCNN To Chest X-ray Images Recognition
| Autor: | WEI, YU-CHENG, 魏瑜成 |
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| Rok vydání: | 2018 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 106 We introduce an innovative application that combines 2 Faster Region with Convolution Neural Network (Faster R-CNN) It is applied to the automatic recognition of chest X-ray images. Because the shape and appearance of lung tumors vary greatly, radiologists have a lot of images to watch every day. Traditional methods cannot automatically and quickly identify tumor locations, but with deep learning, we only need to adapt to different tumor phases and cases through the training set. Compared to other traditional recognition methods, CNN is excellent in target recognition and it became the preferred algorithm in many target recognition challenge. Faster R-CNN uses the CNN to extract the image features, improves the region proposal method, shares the convolution feature with the Fast R-CNN, and makes the target detection and recognition almost instantaneous. This study details the Faster R-CNN and is used for chest X-ray images. We tested the clinical data provided by the doctors and the results confirmed that our approach achieved a certain level of accuracy in a fully automated challenge with very competitive execution time. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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