Combining CNN Features with Voting Classifiers for Optimizing Performance of Brain Tumor Classification
Brain tumors and other nervous system cancers are among the top ten leading fatal diseases. The effective treatment of brain tumors depends on their early detection. This research work makes use of 13 features with a voting classifier that combines logistic regression with stochastic gradient descen...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-03-01
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| Series: | Cancers |
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| Online Access: | https://www.mdpi.com/2072-6694/15/6/1767 |
| _version_ | 1797613021516791808 |
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| author | Nazik Alturki Muhammad Umer Abid Ishaq Nihal Abuzinadah Khaled Alnowaiser Abdullah Mohamed Oumaima Saidani Imran Ashraf |
| author_facet | Nazik Alturki Muhammad Umer Abid Ishaq Nihal Abuzinadah Khaled Alnowaiser Abdullah Mohamed Oumaima Saidani Imran Ashraf |
| author_sort | Nazik Alturki |
| collection | DOAJ |
| description | Brain tumors and other nervous system cancers are among the top ten leading fatal diseases. The effective treatment of brain tumors depends on their early detection. This research work makes use of 13 features with a voting classifier that combines logistic regression with stochastic gradient descent using features extracted by deep convolutional layers for the efficient classification of tumorous victims from the normal. From the first and second-order brain tumor features, deep convolutional features are extracted for model training. Using deep convolutional features helps to increase the precision of tumor and non-tumor patient classification. The proposed voting classifier along with convoluted features produces results that show the highest accuracy of 99.9%. Compared to cutting-edge methods, the proposed approach has demonstrated improved accuracy. |
| first_indexed | 2024-03-11T06:49:05Z |
| format | Article |
| id | doaj.art-df0e6fc9cec8431199139012fccfe9e5 |
| institution | Directory Open Access Journal |
| issn | 2072-6694 |
| language | English |
| last_indexed | 2024-03-11T06:49:05Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Cancers |
| spelling | doaj.art-df0e6fc9cec8431199139012fccfe9e52023-11-17T10:06:55ZengMDPI AGCancers2072-66942023-03-01156176710.3390/cancers15061767Combining CNN Features with Voting Classifiers for Optimizing Performance of Brain Tumor ClassificationNazik Alturki0Muhammad Umer1Abid Ishaq2Nihal Abuzinadah3Khaled Alnowaiser4Abdullah Mohamed5Oumaima Saidani6Imran Ashraf7Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaDepartment of Computer Science & Information Technology, The Islamia University of Bahawalpur, Bahawalpur 63100, PakistanDepartment of Computer Science & Information Technology, The Islamia University of Bahawalpur, Bahawalpur 63100, PakistanFaculty of Computer Science and Information Technology, King Abdulaziz University, P.O. Box. 80200, Jeddah 21589, Saudi ArabiaDepartment of Computer Engineering, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi ArabiaResearch Centre, Future University in Egypt, New Cairo 11745, EgyptDepartment of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaDepartment of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Republic of KoreaBrain tumors and other nervous system cancers are among the top ten leading fatal diseases. The effective treatment of brain tumors depends on their early detection. This research work makes use of 13 features with a voting classifier that combines logistic regression with stochastic gradient descent using features extracted by deep convolutional layers for the efficient classification of tumorous victims from the normal. From the first and second-order brain tumor features, deep convolutional features are extracted for model training. Using deep convolutional features helps to increase the precision of tumor and non-tumor patient classification. The proposed voting classifier along with convoluted features produces results that show the highest accuracy of 99.9%. Compared to cutting-edge methods, the proposed approach has demonstrated improved accuracy.https://www.mdpi.com/2072-6694/15/6/1767brain tumor predictionhealthcaredeep convolutional featuresensemble learning |
| spellingShingle | Nazik Alturki Muhammad Umer Abid Ishaq Nihal Abuzinadah Khaled Alnowaiser Abdullah Mohamed Oumaima Saidani Imran Ashraf Combining CNN Features with Voting Classifiers for Optimizing Performance of Brain Tumor Classification Cancers brain tumor prediction healthcare deep convolutional features ensemble learning |
| title | Combining CNN Features with Voting Classifiers for Optimizing Performance of Brain Tumor Classification |
| title_full | Combining CNN Features with Voting Classifiers for Optimizing Performance of Brain Tumor Classification |
| title_fullStr | Combining CNN Features with Voting Classifiers for Optimizing Performance of Brain Tumor Classification |
| title_full_unstemmed | Combining CNN Features with Voting Classifiers for Optimizing Performance of Brain Tumor Classification |
| title_short | Combining CNN Features with Voting Classifiers for Optimizing Performance of Brain Tumor Classification |
| title_sort | combining cnn features with voting classifiers for optimizing performance of brain tumor classification |
| topic | brain tumor prediction healthcare deep convolutional features ensemble learning |
| url | https://www.mdpi.com/2072-6694/15/6/1767 |
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