The purpose is trying to classify the Liver Fibrosis stage using Gadoxetic Acid-EnhancedMR Images. In the very beginning, a method proposed by one Korean group is being examined and trying to reproduce their result. However, the performance is not as impressive as theirs. Then, some gray-scale image feature extraction methods are used. Last but not least, the hottest method in recent years - ConvolutionNeural Network(CNN) was utilized. Finally, the performance has been evaluated in both methods. The result shows that with manual feature extraction, the Adaboost model works pretty well that AUC achieves 0.9. Besides, the AUC of ResNet-18 network - a deep learning architecture, can reach 0.93. Also, all the hyperparameters and training settings used on ResNet-18 can be transferred to ResNet-50/ResNet-101/InceptionV3 very well. The best model that can be obtained is ResNet-101which has an AUC of 0.96 - higher than all current publications for machine learning methods for staging liver fibrosis.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:liu-162989 |
| Date | January 2019 |
| Creators | Lu, Yi Cheng |
| Publisher | Linköpings universitet, Institutionen för medicin och hälsa, Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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