Biomedical Diagnosis of Breast Cancer Using Deep Learning and Multiple Classifiers
Breast cancer is considered one of the deadliest diseases in women. Due to the risk and threat it poses, the world has agreed to hold a breast cancer awareness day in October, encouraging women to perform mammogram inspections. This inspection may prevent breast-cancer-related deaths or reduce the d...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/12/11/2863 |
| _version_ | 1797465601723072512 |
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| author | Ahmed A. Alsheikhy Yahia Said Tawfeeq Shawly A. Khuzaim Alzahrani Husam Lahza |
| author_facet | Ahmed A. Alsheikhy Yahia Said Tawfeeq Shawly A. Khuzaim Alzahrani Husam Lahza |
| author_sort | Ahmed A. Alsheikhy |
| collection | DOAJ |
| description | Breast cancer is considered one of the deadliest diseases in women. Due to the risk and threat it poses, the world has agreed to hold a breast cancer awareness day in October, encouraging women to perform mammogram inspections. This inspection may prevent breast-cancer-related deaths or reduce the death rate. The identification and classification of breast cancer are challenging tasks. The most commonly known procedure of breast cancer detection is performed by using mammographic images. Recently implemented algorithms suffer from generating accuracy below expectations, and their computational complexity is high. To resolve these issues, this paper proposes a fully automated biomedical diagnosis system of breast cancer using an AlexNet, a type of Convolutional Neural Network (CNN), and multiple classifiers to identify and classify breast cancer. This system utilizes a neuro-fuzzy method, a segmentation algorithm, and various classifiers to reach a higher accuracy than other systems have achieved. Numerous features are extracted to detect and categorize breast cancer. Three datasets from Kaggle were tested to validate the proposed system. The performance evaluation is performed with quantitative and qualitative accuracy, precision, recall, specificity, and F-score. In addition, a comparative assessment is performed between the proposed system and some works of literature. This assessment shows that the presented algorithm provides better classification results and outperforms other systems in all parameters. Its average accuracy is over 98.6%, while other metrics are more than 98%. This research indicates that this approach can be applied to assist doctors in diagnosing breast cancer correctly. |
| first_indexed | 2024-03-09T18:23:50Z |
| format | Article |
| id | doaj.art-cf874e802dff4a42998ea09cf310bcd7 |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-09T18:23:50Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-cf874e802dff4a42998ea09cf310bcd72023-11-24T08:05:30ZengMDPI AGDiagnostics2075-44182022-11-011211286310.3390/diagnostics12112863Biomedical Diagnosis of Breast Cancer Using Deep Learning and Multiple ClassifiersAhmed A. Alsheikhy0Yahia Said1Tawfeeq Shawly2A. Khuzaim Alzahrani3Husam Lahza4Department of Electrical Engineering, College of Engineering, Northern Border University, Arar 91431, Saudi ArabiaDepartment of Electrical Engineering, College of Engineering, Northern Border University, Arar 91431, Saudi ArabiaDepartment of Electrical Engineering, Faculty of Engineering at Rabigh, King Abdulaziz University, Jeddah 21589, Saudi ArabiaDepartment of Medical Laboratory Technology, Faculty of Applied Medical Sciences, Northern Border University, Arar 91431, Saudi ArabiaDepartment of Information Technology, College of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi ArabiaBreast cancer is considered one of the deadliest diseases in women. Due to the risk and threat it poses, the world has agreed to hold a breast cancer awareness day in October, encouraging women to perform mammogram inspections. This inspection may prevent breast-cancer-related deaths or reduce the death rate. The identification and classification of breast cancer are challenging tasks. The most commonly known procedure of breast cancer detection is performed by using mammographic images. Recently implemented algorithms suffer from generating accuracy below expectations, and their computational complexity is high. To resolve these issues, this paper proposes a fully automated biomedical diagnosis system of breast cancer using an AlexNet, a type of Convolutional Neural Network (CNN), and multiple classifiers to identify and classify breast cancer. This system utilizes a neuro-fuzzy method, a segmentation algorithm, and various classifiers to reach a higher accuracy than other systems have achieved. Numerous features are extracted to detect and categorize breast cancer. Three datasets from Kaggle were tested to validate the proposed system. The performance evaluation is performed with quantitative and qualitative accuracy, precision, recall, specificity, and F-score. In addition, a comparative assessment is performed between the proposed system and some works of literature. This assessment shows that the presented algorithm provides better classification results and outperforms other systems in all parameters. Its average accuracy is over 98.6%, while other metrics are more than 98%. This research indicates that this approach can be applied to assist doctors in diagnosing breast cancer correctly.https://www.mdpi.com/2075-4418/12/11/2863breast cancerbiomedical diagnosisCNNfuzzy algorithmBCIC |
| spellingShingle | Ahmed A. Alsheikhy Yahia Said Tawfeeq Shawly A. Khuzaim Alzahrani Husam Lahza Biomedical Diagnosis of Breast Cancer Using Deep Learning and Multiple Classifiers Diagnostics breast cancer biomedical diagnosis CNN fuzzy algorithm BCIC |
| title | Biomedical Diagnosis of Breast Cancer Using Deep Learning and Multiple Classifiers |
| title_full | Biomedical Diagnosis of Breast Cancer Using Deep Learning and Multiple Classifiers |
| title_fullStr | Biomedical Diagnosis of Breast Cancer Using Deep Learning and Multiple Classifiers |
| title_full_unstemmed | Biomedical Diagnosis of Breast Cancer Using Deep Learning and Multiple Classifiers |
| title_short | Biomedical Diagnosis of Breast Cancer Using Deep Learning and Multiple Classifiers |
| title_sort | biomedical diagnosis of breast cancer using deep learning and multiple classifiers |
| topic | breast cancer biomedical diagnosis CNN fuzzy algorithm BCIC |
| url | https://www.mdpi.com/2075-4418/12/11/2863 |
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