碩士 / 義守大學 / 資訊工程學系 / 107 / In clinics, the quality of ultrasound image is usually subjective determination. So far, the quality of ultrasound image is not clearly defined proper criteria. In this study, ResNet50 approach and logistic regression model were used to classify the quality of ultrasound images.
The SonoSite 180+ and curve probe with frequency 2-5 MHz were used to be imaging instruments. Experimental ultrasound images were obtained from a human abdominal prosthesis (E-130328-368T). The locations of imaging were along with epigastric region. The organs were including liver, gallbladder, pancreas, spleen and kidney. The depth of imaging were including 22cm, 20cm, 17cm, 15cm and 12cm. A total of 268 images were obtained in which both high-quality and low-quality images were 134 individually. The number of images among training, validation, and testing sets were 140, 70, and 70. Each image was calculated signal-to-noise ratio (SNR), contrast ratio (CR) and total variation (TV). One Logistic Regression (LR) was applied to establish classified model for groups between high-quality and low-quality. Meanwhile, the classifiers between high-quality and low-quality groups were adopted sequence searching schema in order to obtained suitable parameters for ResNet50 via transfer learning procedure on validation set. Next, the ResNet50, a convolutional neural network algorithm, was also utilized to build a classifier for groups. The last approach was combined ResNet50 and support vector machine (SVM) to construct a classifier for groups (ResNet50+SVM). The performance of these classifiers was investigation of accuracy, specificity, and sensitivity on testing set.
The accuracy, sensitivity, and specificity were 97%, 99%, and 94% provided by LR model with predictors SNR and TV on whole sample. The accuracy, sensitivity, and specificity were 98%, 96%, and 100% provided by ResNet50 model on testing sample. The accuracy, sensitivity, and specificity were 98%, 100%, and 96% provided by ResNet50+SVM model on testing sample.
In this work, the presented methods were generated recommendable accuracy. Moreover, the performances of ResNet50 and ResNet50+SVM were simpler and more accurate than those of LR. Meanwhile, these presented methods were approved to provide objective judgment for quality of ultrasonic image.
| Identifer | oai:union.ndltd.org:TW/107ISU05392010 |
| Date | January 2019 |
| Creators | Zih-Syuan Huang, 黃梓玹 |
| Contributors | Tai-Been Chen, 陳泰賓 |
| Source Sets | National Digital Library of Theses and Dissertations in Taiwan |
| Language | zh-TW |
| Detected Language | English |
| Type | 學位論文 ; thesis |
| Format | 43 |
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