A Deep Learning Approach for Detecting Stroke from Brain CT Images Using OzNet
A brain stroke is a life-threatening medical disorder caused by the inadequate blood supply to the brain. After the stroke, the damaged area of the brain will not operate normally. As a result, early detection is crucial for more effective therapy. Computed tomography (CT) images supply a rapid diag...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Bioengineering |
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| Online Access: | https://www.mdpi.com/2306-5354/9/12/783 |
| _version_ | 1797461421927170048 |
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| author | Oznur Ozaltin Orhan Coskun Ozgur Yeniay Abdulhamit Subasi |
| author_facet | Oznur Ozaltin Orhan Coskun Ozgur Yeniay Abdulhamit Subasi |
| author_sort | Oznur Ozaltin |
| collection | DOAJ |
| description | A brain stroke is a life-threatening medical disorder caused by the inadequate blood supply to the brain. After the stroke, the damaged area of the brain will not operate normally. As a result, early detection is crucial for more effective therapy. Computed tomography (CT) images supply a rapid diagnosis of brain stroke. However, while doctors are analyzing each brain CT image, time is running fast. This circumstance may lead to result in a delay in treatment and making errors. Therefore, we targeted the utilization of an efficient artificial intelligence algorithm in stroke detection. In this paper, we designed hybrid algorithms that include a new convolution neural networks (CNN) architecture called OzNet and various machine learning algorithms for binary classification of real brain stroke CT images. When we classified the dataset with OzNet, we acquired successful performance. However, for this target, we combined it with a minimum Redundancy Maximum Relevance (mRMR) method and Decision Tree (DT), k-Nearest Neighbors (kNN), Linear Discriminant Analysis (LDA), Naïve Bayes (NB), and Support Vector Machines (SVM). In addition, 4096 significant features were obtained from the fully connected layer of OzNet, and we reduced the dimension of features from 4096 to 250 using the mRMR method. Finally, we utilized these machine learning algorithms to classify important features. As a result, OzNet-mRMR-NB was an excellent hybrid algorithm and achieved an accuracy of 98.42% and AUC of 0.99 to detect stroke from brain CT images. |
| first_indexed | 2024-03-09T17:19:11Z |
| format | Article |
| id | doaj.art-66a92fd8d84b467ca63f43eaf066c1f3 |
| institution | Directory Open Access Journal |
| issn | 2306-5354 |
| language | English |
| last_indexed | 2024-03-09T17:19:11Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj.art-66a92fd8d84b467ca63f43eaf066c1f32023-11-24T13:20:58ZengMDPI AGBioengineering2306-53542022-12-0191278310.3390/bioengineering9120783A Deep Learning Approach for Detecting Stroke from Brain CT Images Using OzNetOznur Ozaltin0Orhan Coskun1Ozgur Yeniay2Abdulhamit Subasi3Institute of Science, Department of Statistics, Beytepe Campus, Hacettepe University, Ankara 06800, TurkeyGaziosmanpasa Training and Research Hospital, Pediatric Neurology, Health Sciences University, Gaziosmanpasa, Istanbul 34779, TurkeyInstitute of Science, Department of Statistics, Beytepe Campus, Hacettepe University, Ankara 06800, TurkeyInstitute of Biomedicine, Faculty of Medicine, University of Turku, 20520 Turku, FinlandA brain stroke is a life-threatening medical disorder caused by the inadequate blood supply to the brain. After the stroke, the damaged area of the brain will not operate normally. As a result, early detection is crucial for more effective therapy. Computed tomography (CT) images supply a rapid diagnosis of brain stroke. However, while doctors are analyzing each brain CT image, time is running fast. This circumstance may lead to result in a delay in treatment and making errors. Therefore, we targeted the utilization of an efficient artificial intelligence algorithm in stroke detection. In this paper, we designed hybrid algorithms that include a new convolution neural networks (CNN) architecture called OzNet and various machine learning algorithms for binary classification of real brain stroke CT images. When we classified the dataset with OzNet, we acquired successful performance. However, for this target, we combined it with a minimum Redundancy Maximum Relevance (mRMR) method and Decision Tree (DT), k-Nearest Neighbors (kNN), Linear Discriminant Analysis (LDA), Naïve Bayes (NB), and Support Vector Machines (SVM). In addition, 4096 significant features were obtained from the fully connected layer of OzNet, and we reduced the dimension of features from 4096 to 250 using the mRMR method. Finally, we utilized these machine learning algorithms to classify important features. As a result, OzNet-mRMR-NB was an excellent hybrid algorithm and achieved an accuracy of 98.42% and AUC of 0.99 to detect stroke from brain CT images.https://www.mdpi.com/2306-5354/9/12/783brain strokeclassificationconvolution neural networkscomputed tomographyfeature extractionmRMR |
| spellingShingle | Oznur Ozaltin Orhan Coskun Ozgur Yeniay Abdulhamit Subasi A Deep Learning Approach for Detecting Stroke from Brain CT Images Using OzNet Bioengineering brain stroke classification convolution neural networks computed tomography feature extraction mRMR |
| title | A Deep Learning Approach for Detecting Stroke from Brain CT Images Using OzNet |
| title_full | A Deep Learning Approach for Detecting Stroke from Brain CT Images Using OzNet |
| title_fullStr | A Deep Learning Approach for Detecting Stroke from Brain CT Images Using OzNet |
| title_full_unstemmed | A Deep Learning Approach for Detecting Stroke from Brain CT Images Using OzNet |
| title_short | A Deep Learning Approach for Detecting Stroke from Brain CT Images Using OzNet |
| title_sort | deep learning approach for detecting stroke from brain ct images using oznet |
| topic | brain stroke classification convolution neural networks computed tomography feature extraction mRMR |
| url | https://www.mdpi.com/2306-5354/9/12/783 |
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