Deep learning architectures for object detection with applications to MRI scans
This report presents the results detailing the effectiveness of the Single Shot Multibox Detector (SSD) [4] in detecting brain tumor objects from T1 weighted MRI scans. The report will first detail an extensive literature review of four different architectures that utilize Convolutional Neural Netwo...
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sg-ntu-dr.10356-765512023-03-03T20:34:04Z Deep learning architectures for object detection with applications to MRI scans Ng, Tze Yang Jagath C. Rajapakse School of Computer Science and Engineering DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence This report presents the results detailing the effectiveness of the Single Shot Multibox Detector (SSD) [4] in detecting brain tumor objects from T1 weighted MRI scans. The report will first detail an extensive literature review of four different architectures that utilize Convolutional Neural Networks to perform object detection in images – Region with CNN Features (RCNN) [2], Fast-RCNN [3], Faster-RCNN [7] and the SSD. For each architecture, this report will elaborate on its’ architectural design, its performance in terms of speed and accuracy on the PASCAL VOC dataset, training and test times, and its’ limitations. This report will also show how each successive architecture manages to improve on the limitation of its predecessor to improve on its performance. We test implementations of the Faster-RCNN and SSD architecture on the PASCAL VOC data set and achieve a reasonable 70.9% and 70.6% mAP respectively. We also train the two architectures on brain T1 MRI images obtained from the BraTS 2018 data set to test its performance in detecting gliomas (brain tumor). These images were segmented in the Sagittal, Coronal and Axial plane. For this we achieve a 2.57% mean Average Precision (mAP) for Faster-RCNN and 24.3% mAP for SSD. Bachelor of Engineering (Computer Engineering) 2019-03-26T05:08:50Z 2019-03-26T05:08:50Z 2019 Final Year Project (FYP) http://hdl.handle.net/10356/76551 en Nanyang Technological University 37 p. application/pdf |
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DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Ng, Tze Yang Deep learning architectures for object detection with applications to MRI scans |
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This report presents the results detailing the effectiveness of the Single Shot Multibox Detector (SSD) [4] in detecting brain tumor objects from T1 weighted MRI scans. The report will first detail an extensive literature review of four different architectures that utilize Convolutional Neural Networks to perform object detection in images – Region with CNN Features (RCNN) [2], Fast-RCNN [3], Faster-RCNN [7] and the SSD. For each architecture, this report will elaborate on its’ architectural design, its performance in terms of speed and accuracy on the PASCAL VOC dataset, training and test times, and its’ limitations. This report will also show how each successive architecture manages to improve on the limitation of its predecessor to improve on its performance. We test implementations of the Faster-RCNN and SSD architecture on the PASCAL VOC data set and achieve a reasonable 70.9% and 70.6% mAP respectively. We also train the two architectures on brain T1 MRI images obtained from the BraTS 2018 data set to test its performance in detecting gliomas (brain tumor). These images were segmented in the Sagittal, Coronal and Axial plane. For this we achieve a 2.57% mean Average Precision (mAP) for Faster-RCNN and 24.3% mAP for SSD. |
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Jagath C. Rajapakse |
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Jagath C. Rajapakse Ng, Tze Yang |
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Final Year Project |
author |
Ng, Tze Yang |
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Ng, Tze Yang |
title |
Deep learning architectures for object detection with applications to MRI scans |
title_short |
Deep learning architectures for object detection with applications to MRI scans |
title_full |
Deep learning architectures for object detection with applications to MRI scans |
title_fullStr |
Deep learning architectures for object detection with applications to MRI scans |
title_full_unstemmed |
Deep learning architectures for object detection with applications to MRI scans |
title_sort |
deep learning architectures for object detection with applications to mri scans |
publishDate |
2019 |
url |
http://hdl.handle.net/10356/76551 |
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1759855917647527936 |