Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/151137
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dc.contributor.authorTang, Yuanen_US
dc.date.accessioned2021-06-16T08:52:43Z-
dc.date.available2021-06-16T08:52:43Z-
dc.date.issued2021-
dc.identifier.citationTang, Y. (2021). Machine learning based x-ray / CT image analysis. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/151137en_US
dc.identifier.urihttps://hdl.handle.net/10356/151137-
dc.description.abstractMedical image analysis aims to extract clinically relevant information from medical images, such as X-ray, CT, or MRI images. The project focuses on two domains of medical image analysis, image segmentation and classification. Medical image segmentation is a process to determine regions or boundaries of objects of interest, which is the first step in many clinical decision systems. Medical image classification performs disease detection on medical images to assess the probability of certain diseases. In this project, two networks are proposed to improve the existing methods on multi-organ segmentation on x-ray and CT images and disease classification on chest X-ray images. Existing segmentation networks consist of an encoder to extract features from input images, while a decoder decodes the features and outputs pixel-wise segmentation masks. We proposed a generative adversarial segmentation network that improves the performance of traditional segmentation networks with the aid of unlabelled data. Inspired by segmentation networks, we have also proposed an attention-based classification network. The network utilises the location information, such as organ mask, region of interest (ROI) bounding box to improve the classification performance while generating disease localization heatmaps.en_US
dc.language.isoenen_US
dc.publisherNanyang Technological Universityen_US
dc.subjectEngineering::Electrical and electronic engineeringen_US
dc.titleMachine learning based x-ray / CT image analysisen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorHuang Weiminen_US
dc.contributor.supervisorLin Zhipingen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeBachelor of Engineering (Electrical and Electronic Engineering)en_US
dc.contributor.supervisoremailEZPLin@ntu.edu.sg, MWMHuang@ntu.edu.sgen_US
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Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)
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