Medical image recognition based on Deep Learning using Torch
| Autor: | Chen-Cheng Huang, 黃晨誠 |
|---|---|
| Rok vydání: | 2017 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 105 Many scholars actively engaged in the development of the use of medical imaging-based computer-aided diagnostic system. In the past, these computer-assisted systems use traditional classifiers to perform medical image analysis and recognition Doctors can not only rely on clinical experience, but also through the computer-aided system analysis results as a basis for diagnosis. In recent years, computer technology has been a breakthrough. So, deep learning in artificial intelligence is becoming mainstream and gradually applied in many areas such as computer vision, pattern recognition, speech recognition and so on. In this thesis, we propose a medical image recognition system based on deep learning using Torch system. Our system has a higher recognition accuracy, and through the CUDA technology it makes the learning speed can be greatly improved. The recognition algorithm of our system is convolution neural network (CNN). We use the Torch developed by Facebook to achieve CNN. The medical datasets is provided by chung shan medical university. It is divided into three parts: the dataset of benign and malignant tumor of Nasopharyngeal Carcinoma, the dataset of lung adenocarcinoma EGFR gene mutation, and ichemic stroke datasets. Finally, we use data augmentation method to increase the amount of image data. The contribution of thesis is to apply deep learning method to analysis medical image. And in the Benign Nasopha-ryngeal Tumors and Malignant Na-sopharyngeal Tumors of Nasopharyngeal Carcinoma data set classification accuracy reached 68.75%, the classification accuracy of EGFR mutation data of lung adenocarcinoma is 97.656%, and the classification accuracy of Ischemic Stroke data set reached 92.968%. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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