Deep Learning Based Breast Cancer Detection Using Decision Fusion
Breast cancer, which has the highest mortality and morbidity rates among diseases affecting women, poses a significant threat to their lives and health. Early diagnosis is crucial for effective treatment. Recent advancements in artificial intelligence have enabled innovative techniques for early bre...
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| Format: | Article |
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MDPI AG
2024-11-01
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| Series: | Computers |
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| Online Access: | https://www.mdpi.com/2073-431X/13/11/294 |
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| author | Doğu Manalı Hasan Demirel Alaa Eleyan |
| author_facet | Doğu Manalı Hasan Demirel Alaa Eleyan |
| author_sort | Doğu Manalı |
| collection | DOAJ |
| description | Breast cancer, which has the highest mortality and morbidity rates among diseases affecting women, poses a significant threat to their lives and health. Early diagnosis is crucial for effective treatment. Recent advancements in artificial intelligence have enabled innovative techniques for early breast cancer detection. Convolutional neural networks (CNNs) and support vector machines (SVMs) have been used in computer-aided diagnosis (CAD) systems to identify breast tumors from mammograms. However, existing methods often face challenges in accuracy and reliability across diverse diagnostic scenarios. This paper proposes a three parallel channel artificial intelligence-based system. First, SVM distinguishes between different tumor types using local binary pattern (LBP) features. Second, a pre-trained CNN extracts features, and SVM identifies potential tumors. Third, a newly developed CNN is trained and used to classify mammogram images. Finally, a decision fusion that combines results from the three channels to enhance system performance is implemented using different rules. The proposed decision fusion-based system outperforms state-of-the-art alternatives with an overall accuracy of 99.1% using the product rule. |
| format | Article |
| id | doaj-art-5fbc3e41270d424ea208883fd197b867 |
| institution | OA Journals |
| issn | 2073-431X |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computers |
| spelling | doaj-art-5fbc3e41270d424ea208883fd197b8672025-08-20T01:53:44ZengMDPI AGComputers2073-431X2024-11-01131129410.3390/computers13110294Deep Learning Based Breast Cancer Detection Using Decision FusionDoğu Manalı0Hasan Demirel1Alaa Eleyan2Department of Electrical and Electronic Engineering, Eastern Mediterranean University, Famagusta 99628, TurkeyDepartment of Electrical and Electronic Engineering, Eastern Mediterranean University, Famagusta 99628, TurkeyCollege of Engineering and Technology, American University of the Middle East, Egaila 54200, KuwaitBreast cancer, which has the highest mortality and morbidity rates among diseases affecting women, poses a significant threat to their lives and health. Early diagnosis is crucial for effective treatment. Recent advancements in artificial intelligence have enabled innovative techniques for early breast cancer detection. Convolutional neural networks (CNNs) and support vector machines (SVMs) have been used in computer-aided diagnosis (CAD) systems to identify breast tumors from mammograms. However, existing methods often face challenges in accuracy and reliability across diverse diagnostic scenarios. This paper proposes a three parallel channel artificial intelligence-based system. First, SVM distinguishes between different tumor types using local binary pattern (LBP) features. Second, a pre-trained CNN extracts features, and SVM identifies potential tumors. Third, a newly developed CNN is trained and used to classify mammogram images. Finally, a decision fusion that combines results from the three channels to enhance system performance is implemented using different rules. The proposed decision fusion-based system outperforms state-of-the-art alternatives with an overall accuracy of 99.1% using the product rule.https://www.mdpi.com/2073-431X/13/11/294mammography image classificationbreast cancerconvolutional neural networkssupport vector machineartificial intelligencedeep learning |
| spellingShingle | Doğu Manalı Hasan Demirel Alaa Eleyan Deep Learning Based Breast Cancer Detection Using Decision Fusion Computers mammography image classification breast cancer convolutional neural networks support vector machine artificial intelligence deep learning |
| title | Deep Learning Based Breast Cancer Detection Using Decision Fusion |
| title_full | Deep Learning Based Breast Cancer Detection Using Decision Fusion |
| title_fullStr | Deep Learning Based Breast Cancer Detection Using Decision Fusion |
| title_full_unstemmed | Deep Learning Based Breast Cancer Detection Using Decision Fusion |
| title_short | Deep Learning Based Breast Cancer Detection Using Decision Fusion |
| title_sort | deep learning based breast cancer detection using decision fusion |
| topic | mammography image classification breast cancer convolutional neural networks support vector machine artificial intelligence deep learning |
| url | https://www.mdpi.com/2073-431X/13/11/294 |
| work_keys_str_mv | AT dogumanalı deeplearningbasedbreastcancerdetectionusingdecisionfusion AT hasandemirel deeplearningbasedbreastcancerdetectionusingdecisionfusion AT alaaeleyan deeplearningbasedbreastcancerdetectionusingdecisionfusion |