| Summary: | 碩士 === 國立中央大學 === 資訊管理學系在職專班 === 107 === Liver disease is one of the major civilized diseases in Taiwanese society. Even more, the percentage of adult male workers in Taiwan who have fatty liver is up to 49%. In the past years, blood tests and liver slices are the most often used for fatty liver screening. However, intrusive inspection methods not only cause discomfort but also high costs and potential risk to the patients.
This thesis proposes a deep learning method which uses a convolutional neural network (CNN) to model and classify liver ultrasound images of 331 patients, and to compare the accuracy of classification models established by machine learning algorithms with their blood test data. Furthermore, this study tries to combine machine learning with deep learning to find a more appropriate way to judge the ultrasound images of liver.
According to the experiment results, applying the SVM classification by the features extracted from CNN has better performance than using only machine learning methods. The accuracy, precision, recall and F1 score achieved 0.82, 0.862, 0.806 and 0.833 which are all better than machine learning methods with blood test data. Thus, it has a potential to diagnose fatty liver with CNN.
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