Image thresholding improves 3-dimensional convolutional neural network diagnosis of different acute brain hemorrhages on computed tomography scans
Intracranial hemorrhage is a medical emergency that requires urgent diagnosis and immediate treatment to improve patient outcome. Machine learning algorithms can be used to perform medical image classification and assist clinicians in diagnosing radiological scans. In this paper, we apply 3-dimensio...
| Main Authors: | , , , , , |
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| Format: | Journal Article |
| Language: | English |
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2019
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| Online Access: | https://hdl.handle.net/10356/105923 http://hdl.handle.net/10220/48796 http://dx.doi.org/10.3390/s19092167 |
| _version_ | 1811684759602987008 |
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| author | Ker, Justin Bai, Yeqi Rao, Jai Lim, Tchoyoson Singh, Satya Prakash Wang, Lipo |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Ker, Justin Bai, Yeqi Rao, Jai Lim, Tchoyoson Singh, Satya Prakash Wang, Lipo |
| author_sort | Ker, Justin |
| collection | NTU |
| description | Intracranial hemorrhage is a medical emergency that requires urgent diagnosis and immediate treatment to improve patient outcome. Machine learning algorithms can be used to perform medical image classification and assist clinicians in diagnosing radiological scans. In this paper, we apply 3-dimensional convolutional neural networks (3D CNN) to classify computed tomography (CT) brain scans into normal scans (N) and abnormal scans containing subarachnoid hemorrhage (SAH), intraparenchymal hemorrhage (IPH), acute subdural hemorrhage (ASDH) and brain polytrauma hemorrhage (BPH). The dataset used consists of 399 volumetric CT brain images representing approximately 12,000 images from the National Neuroscience Institute, Singapore. We used a 3D CNN to perform both 2-class (normal versus a specific abnormal class) and 4-class classification (between normal, SAH, IPH, ASDH). We apply image thresholding at the image pre-processing step, that improves 3D CNN classification accuracy and performance by accentuating the pixel intensities that contribute most to feature discrimination. For 2-class classification, the F1 scores for various pairs of medical diagnoses ranged from 0.706 to 0.902 without thresholding. With thresholding implemented, the F1 scores improved and ranged from 0.919 to 0.952. Our results are comparable to, and in some cases, exceed the results published in other work applying 3D CNN to CT or magnetic resonance imaging (MRI) brain scan classification. This work represents a direct application of a 3D CNN to a real hospital scenario involving a medically emergent CT brain diagnosis |
| first_indexed | 2024-10-01T04:33:44Z |
| format | Journal Article |
| id | ntu-10356/105923 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:33:44Z |
| publishDate | 2019 |
| record_format | dspace |
| spelling | ntu-10356/1059232019-12-06T22:00:44Z Image thresholding improves 3-dimensional convolutional neural network diagnosis of different acute brain hemorrhages on computed tomography scans Ker, Justin Bai, Yeqi Rao, Jai Lim, Tchoyoson Singh, Satya Prakash Wang, Lipo School of Electrical and Electronic Engineering Machine Learning 3D Convolutional Neural Networks DRNTU::Engineering::Electrical and electronic engineering Intracranial hemorrhage is a medical emergency that requires urgent diagnosis and immediate treatment to improve patient outcome. Machine learning algorithms can be used to perform medical image classification and assist clinicians in diagnosing radiological scans. In this paper, we apply 3-dimensional convolutional neural networks (3D CNN) to classify computed tomography (CT) brain scans into normal scans (N) and abnormal scans containing subarachnoid hemorrhage (SAH), intraparenchymal hemorrhage (IPH), acute subdural hemorrhage (ASDH) and brain polytrauma hemorrhage (BPH). The dataset used consists of 399 volumetric CT brain images representing approximately 12,000 images from the National Neuroscience Institute, Singapore. We used a 3D CNN to perform both 2-class (normal versus a specific abnormal class) and 4-class classification (between normal, SAH, IPH, ASDH). We apply image thresholding at the image pre-processing step, that improves 3D CNN classification accuracy and performance by accentuating the pixel intensities that contribute most to feature discrimination. For 2-class classification, the F1 scores for various pairs of medical diagnoses ranged from 0.706 to 0.902 without thresholding. With thresholding implemented, the F1 scores improved and ranged from 0.919 to 0.952. Our results are comparable to, and in some cases, exceed the results published in other work applying 3D CNN to CT or magnetic resonance imaging (MRI) brain scan classification. This work represents a direct application of a 3D CNN to a real hospital scenario involving a medically emergent CT brain diagnosis Published version 2019-06-18T04:35:37Z 2019-12-06T22:00:44Z 2019-06-18T04:35:37Z 2019-12-06T22:00:44Z 2019 Journal Article Ker, J., Singh, S. P., Bai, Y., Rao, J., Lim, T., & Wang, L. (2019). Image thresholding improves 3-dimensional convolutional neural network diagnosis of different acute brain hemorrhages on computed tomography scans. Sensors, 19(9), 2167-. doi:10.3390/s19092167 1424-8220 https://hdl.handle.net/10356/105923 http://hdl.handle.net/10220/48796 http://dx.doi.org/10.3390/s19092167 en Sensors © 2019 The Authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). 12 p. application/pdf |
| spellingShingle | Machine Learning 3D Convolutional Neural Networks DRNTU::Engineering::Electrical and electronic engineering Ker, Justin Bai, Yeqi Rao, Jai Lim, Tchoyoson Singh, Satya Prakash Wang, Lipo Image thresholding improves 3-dimensional convolutional neural network diagnosis of different acute brain hemorrhages on computed tomography scans |
| title | Image thresholding improves 3-dimensional convolutional neural network diagnosis of different acute brain hemorrhages on computed tomography scans |
| title_full | Image thresholding improves 3-dimensional convolutional neural network diagnosis of different acute brain hemorrhages on computed tomography scans |
| title_fullStr | Image thresholding improves 3-dimensional convolutional neural network diagnosis of different acute brain hemorrhages on computed tomography scans |
| title_full_unstemmed | Image thresholding improves 3-dimensional convolutional neural network diagnosis of different acute brain hemorrhages on computed tomography scans |
| title_short | Image thresholding improves 3-dimensional convolutional neural network diagnosis of different acute brain hemorrhages on computed tomography scans |
| title_sort | image thresholding improves 3 dimensional convolutional neural network diagnosis of different acute brain hemorrhages on computed tomography scans |
| topic | Machine Learning 3D Convolutional Neural Networks DRNTU::Engineering::Electrical and electronic engineering |
| url | https://hdl.handle.net/10356/105923 http://hdl.handle.net/10220/48796 http://dx.doi.org/10.3390/s19092167 |
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