Breast Cancer Detection Using Convoluted Features and Ensemble Machine Learning Algorithm
Breast cancer is a common cause of female mortality in developing countries. Screening and early diagnosis can play an important role in the prevention and treatment of these cancers. This study proposes an ensemble learning-based voting classifier that combines the logistic regression and stochasti...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-12-01
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| Series: | Cancers |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-6694/14/23/6015 |
| _version_ | 1797463471489548288 |
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| author | Muhammad Umer Mahum Naveed Fadwa Alrowais Abid Ishaq Abdullah Al Hejaili Shtwai Alsubai Ala’ Abdulmajid Eshmawi Abdullah Mohamed Imran Ashraf |
| author_facet | Muhammad Umer Mahum Naveed Fadwa Alrowais Abid Ishaq Abdullah Al Hejaili Shtwai Alsubai Ala’ Abdulmajid Eshmawi Abdullah Mohamed Imran Ashraf |
| author_sort | Muhammad Umer |
| collection | DOAJ |
| description | Breast cancer is a common cause of female mortality in developing countries. Screening and early diagnosis can play an important role in the prevention and treatment of these cancers. This study proposes an ensemble learning-based voting classifier that combines the logistic regression and stochastic gradient descent classifier with deep convoluted features for the accurate detection of cancerous patients. Deep convoluted features are extracted from the microscopic features and fed to the ensemble voting classifier. This idea provides an optimized framework that accurately classifies malignant and benign tumors with improved accuracy. Results obtained using the voting classifier with convoluted features demonstrate that the highest classification accuracy of 100% is achieved. The proposed approach revealed the accuracy enhancement in comparison with the state-of-the-art approaches. |
| first_indexed | 2024-03-09T17:51:11Z |
| format | Article |
| id | doaj.art-336e8f38ae15450bba627dfc773bbbfd |
| institution | Directory Open Access Journal |
| issn | 2072-6694 |
| language | English |
| last_indexed | 2024-03-09T17:51:11Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Cancers |
| spelling | doaj.art-336e8f38ae15450bba627dfc773bbbfd2023-11-24T10:42:25ZengMDPI AGCancers2072-66942022-12-011423601510.3390/cancers14236015Breast Cancer Detection Using Convoluted Features and Ensemble Machine Learning AlgorithmMuhammad Umer0Mahum Naveed1Fadwa Alrowais2Abid Ishaq3Abdullah Al Hejaili4Shtwai Alsubai5Ala’ Abdulmajid Eshmawi6Abdullah Mohamed7Imran Ashraf8Department of Computer Science & Information Technology, The Islamia University of Bahawalpur, Bahawalpur 63100, PakistanFatima Jinnah Medical University, Lahore 54000, PakistanDepartment of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh 11671, Saudi ArabiaDepartment of Computer Science & Information Technology, The Islamia University of Bahawalpur, Bahawalpur 63100, PakistanComputer Science Department, Faculty of Computers & Information Technology, University of Tabuk, Tabuk 71491, Saudi ArabiaDepartment of Computer Science, College of Computer Engineering and Sciences in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi ArabiaDepartment of Cybersecurity, College of Computer Science and Engineering, University of Jeddah, Jeddah 21959, Saudi ArabiaResearch Centre, Future University in Egypt, New Cairo 11745, EgyptDepartment of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Republic of KoreaBreast cancer is a common cause of female mortality in developing countries. Screening and early diagnosis can play an important role in the prevention and treatment of these cancers. This study proposes an ensemble learning-based voting classifier that combines the logistic regression and stochastic gradient descent classifier with deep convoluted features for the accurate detection of cancerous patients. Deep convoluted features are extracted from the microscopic features and fed to the ensemble voting classifier. This idea provides an optimized framework that accurately classifies malignant and benign tumors with improved accuracy. Results obtained using the voting classifier with convoluted features demonstrate that the highest classification accuracy of 100% is achieved. The proposed approach revealed the accuracy enhancement in comparison with the state-of-the-art approaches.https://www.mdpi.com/2072-6694/14/23/6015breast cancer predictionhealthcaredeep convoluted featuresensemble learning |
| spellingShingle | Muhammad Umer Mahum Naveed Fadwa Alrowais Abid Ishaq Abdullah Al Hejaili Shtwai Alsubai Ala’ Abdulmajid Eshmawi Abdullah Mohamed Imran Ashraf Breast Cancer Detection Using Convoluted Features and Ensemble Machine Learning Algorithm Cancers breast cancer prediction healthcare deep convoluted features ensemble learning |
| title | Breast Cancer Detection Using Convoluted Features and Ensemble Machine Learning Algorithm |
| title_full | Breast Cancer Detection Using Convoluted Features and Ensemble Machine Learning Algorithm |
| title_fullStr | Breast Cancer Detection Using Convoluted Features and Ensemble Machine Learning Algorithm |
| title_full_unstemmed | Breast Cancer Detection Using Convoluted Features and Ensemble Machine Learning Algorithm |
| title_short | Breast Cancer Detection Using Convoluted Features and Ensemble Machine Learning Algorithm |
| title_sort | breast cancer detection using convoluted features and ensemble machine learning algorithm |
| topic | breast cancer prediction healthcare deep convoluted features ensemble learning |
| url | https://www.mdpi.com/2072-6694/14/23/6015 |
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