A Hybrid Method of 1D-CNN and Machine Learning Algorithms for Breast Cancer Detection
Breast cancer is a health concern of importance, and it is crucial to detect it early for effective treatment. Recently there has been increasing interest in using artificial intelligence (AI) for breast cancer detection, which has shown results in enhancing accuracy and reducing false positives. H...
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| Format: | Article |
| Language: | English |
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University of Baghdad, College of Science for Women
2024-10-01
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| Series: | مجلة بغداد للعلوم |
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| Online Access: | https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/9443 |
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| author | Ahmed Adil Nafea Manar AL-Mahdawi Khattab M Ali Alheeti Mustafa S. Ibrahim Alsumaidaie Mohammed M AL-Ani |
| author_facet | Ahmed Adil Nafea Manar AL-Mahdawi Khattab M Ali Alheeti Mustafa S. Ibrahim Alsumaidaie Mohammed M AL-Ani |
| author_sort | Ahmed Adil Nafea |
| collection | DOAJ |
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Breast cancer is a health concern of importance, and it is crucial to detect it early for effective treatment. Recently there has been increasing interest in using artificial intelligence (AI) for breast cancer detection, which has shown results in enhancing accuracy and reducing false positives. However, there are some limitations regarding accuracy in detection. This study introduces an approach that utilizes 1D CNN as feature extraction and employs machine learning (ML) algorithms such as XGBoost, random forests (RF), decision trees (DT) support vector machines (SVM) and k nearest neighbor (KNN) to classify samples as either benign or malignant aiming to enhance accuracy. Our findings reveal that the XGBoost algorithm with feature extraction (1D CNN) achieved an accuracy of 98.24% on the test set. This study highlights the feasibility of employing machine learning algorithms and deep learning (DL). This study uses a dataset of Wisconsin breast cancer (WBC), for detecting breast cancer. The proposed approach has a good detection and improving outcomes via shows accurate and reliable tools for diagnosing breast cancer.
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| format | Article |
| id | doaj-art-3cc65c1fdbad4465811c6973bcdee99c |
| institution | Kabale University |
| issn | 2078-8665 2411-7986 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | University of Baghdad, College of Science for Women |
| record_format | Article |
| series | مجلة بغداد للعلوم |
| spelling | doaj-art-3cc65c1fdbad4465811c6973bcdee99c2025-08-20T03:58:23ZengUniversity of Baghdad, College of Science for Womenمجلة بغداد للعلوم2078-86652411-79862024-10-01211010.21123/bsj.2024.9443A Hybrid Method of 1D-CNN and Machine Learning Algorithms for Breast Cancer DetectionAhmed Adil Nafea0https://orcid.org/0000-0003-2293-1108Manar AL-Mahdawi1Khattab M Ali Alheeti2Mustafa S. Ibrahim Alsumaidaie3Mohammed M AL-Ani4Department of Artificial Intelligence, College of Computer Science and IT, University of Anbar, Ramadi, Iraq.Department of Physics, College of Science, AL-Nahrain University, Baghdad, Iraq. Department of Computer Science, University of Anbar Ramadi, Iraq.Department of Computer Science, University of Anbar Ramadi, Iraq.Center for Artificial Intelligence Technology (CAIT), Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi, Selangor, Malaysia. Breast cancer is a health concern of importance, and it is crucial to detect it early for effective treatment. Recently there has been increasing interest in using artificial intelligence (AI) for breast cancer detection, which has shown results in enhancing accuracy and reducing false positives. However, there are some limitations regarding accuracy in detection. This study introduces an approach that utilizes 1D CNN as feature extraction and employs machine learning (ML) algorithms such as XGBoost, random forests (RF), decision trees (DT) support vector machines (SVM) and k nearest neighbor (KNN) to classify samples as either benign or malignant aiming to enhance accuracy. Our findings reveal that the XGBoost algorithm with feature extraction (1D CNN) achieved an accuracy of 98.24% on the test set. This study highlights the feasibility of employing machine learning algorithms and deep learning (DL). This study uses a dataset of Wisconsin breast cancer (WBC), for detecting breast cancer. The proposed approach has a good detection and improving outcomes via shows accurate and reliable tools for diagnosing breast cancer. https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/9443Breast cancer diagnosis, Deep learning, Machine learning, Wisconsin, 1D-CNN |
| spellingShingle | Ahmed Adil Nafea Manar AL-Mahdawi Khattab M Ali Alheeti Mustafa S. Ibrahim Alsumaidaie Mohammed M AL-Ani A Hybrid Method of 1D-CNN and Machine Learning Algorithms for Breast Cancer Detection مجلة بغداد للعلوم Breast cancer diagnosis, Deep learning, Machine learning, Wisconsin, 1D-CNN |
| title | A Hybrid Method of 1D-CNN and Machine Learning Algorithms for Breast Cancer Detection |
| title_full | A Hybrid Method of 1D-CNN and Machine Learning Algorithms for Breast Cancer Detection |
| title_fullStr | A Hybrid Method of 1D-CNN and Machine Learning Algorithms for Breast Cancer Detection |
| title_full_unstemmed | A Hybrid Method of 1D-CNN and Machine Learning Algorithms for Breast Cancer Detection |
| title_short | A Hybrid Method of 1D-CNN and Machine Learning Algorithms for Breast Cancer Detection |
| title_sort | hybrid method of 1d cnn and machine learning algorithms for breast cancer detection |
| topic | Breast cancer diagnosis, Deep learning, Machine learning, Wisconsin, 1D-CNN |
| url | https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/9443 |
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