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Title: Shallow 3D CNN for detecting acute brain hemorrhage from medical imaging sensors
Authors: Singh, Satya P.
Wang, Lipo
Gupta, Sukrit
Gulyás, Balázs
Padmanabhan, Parasuraman
Keywords: Science::Medicine
Issue Date: 2020
Source: Singh, S. P., Wang, L., Gupta, S., Gulyás, B. & Padmanabhan, P. (2020). Shallow 3D CNN for detecting acute brain hemorrhage from medical imaging sensors. IEEE Sensors Journal, 21(13), 14290-14299.
Project: ADH-11/2017-DSAIR
Journal: IEEE Sensors Journal
Abstract: Successive layers in convolutional neural networks (CNN) extract different features from input images. Applications of CNNs to detect abnormalities in the 2D images or 3D volumes of body organs have recently become popular. However, computer-aided detection of diseases using deep CNN is challenging due to the absence of a large set of training medical images/scans and the relatively small and hard to detect abnormalities. In this paper, we propose a method for normalizing 3D volumetric scans using the intensity profile of the training samples. This aids the CNN by creating a higher contrast around the abnormal region of interest in the scan. We use the CQ500 head CT dataset to demonstrate the validity of our method for detecting different acute brain hemorrhages such as subarachnoid hemorrhage (SAH), intraparenchymal hemorrhage (IPH), subdural hematoma (SDH), and intraventricular hemorrhage (IVH). We compare the proposed method with a baseline, two variants of the 3D VGGNet architectures, Resnet, and show that the proposed method achieves significant improvement in classification performance. For binary classification, we achieved the best F1 score of 0.96 (normal vs SAH), 0.93 (normal vs IPH), 0.98 (normal vs SDH), and 0.99 (normal vs IVH), and for four-class classification, we obtained an average F1 score of 0.77. Finally, we show a limitation of the proposed method while detecting varied abnormalities. The proposed method has applications for abnormality detection for different organs.
ISSN: 1530-437X
DOI: 10.1109/JSEN.2020.3023471
Schools: Lee Kong Chian School of Medicine (LKCMedicine) 
School of Electrical and Electronic Engineering 
School of Computer Science and Engineering 
Research Centres: Cognitive Neuroimaging Centre
Rights: © 2020 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles
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