Hyperparameter Optimizer with Deep Learning-Based Decision-Support Systems for Histopathological Breast Cancer Diagnosis
Histopathological images are commonly used imaging modalities for breast cancer. As manual analysis of histopathological images is difficult, automated tools utilizing artificial intelligence (AI) and deep learning (DL) methods should be modelled. The recent advancements in DL approaches will be hel...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Cancers |
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| Online Access: | https://www.mdpi.com/2072-6694/15/3/885 |
| _version_ | 1797624941342883840 |
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| author | Marwa Obayya Mashael S. Maashi Nadhem Nemri Heba Mohsen Abdelwahed Motwakel Azza Elneil Osman Amani A. Alneil Mohamed Ibrahim Alsaid |
| author_facet | Marwa Obayya Mashael S. Maashi Nadhem Nemri Heba Mohsen Abdelwahed Motwakel Azza Elneil Osman Amani A. Alneil Mohamed Ibrahim Alsaid |
| author_sort | Marwa Obayya |
| collection | DOAJ |
| description | Histopathological images are commonly used imaging modalities for breast cancer. As manual analysis of histopathological images is difficult, automated tools utilizing artificial intelligence (AI) and deep learning (DL) methods should be modelled. The recent advancements in DL approaches will be helpful in establishing maximal image classification performance in numerous application zones. This study develops an arithmetic optimization algorithm with deep-learning-based histopathological breast cancer classification (AOADL-HBCC) technique for healthcare decision making. The AOADL-HBCC technique employs noise removal based on median filtering (MF) and a contrast enhancement process. In addition, the presented AOADL-HBCC technique applies an AOA with a SqueezeNet model to derive feature vectors. Finally, a deep belief network (DBN) classifier with an Adamax hyperparameter optimizer is applied for the breast cancer classification process. In order to exhibit the enhanced breast cancer classification results of the AOADL-HBCC methodology, this comparative study states that the AOADL-HBCC technique displays better performance than other recent methodologies, with a maximum accuracy of 96.77%. |
| first_indexed | 2024-03-11T09:49:47Z |
| format | Article |
| id | doaj.art-17c373df20384ebf9a8d0b84982ee313 |
| institution | Directory Open Access Journal |
| issn | 2072-6694 |
| language | English |
| last_indexed | 2024-03-11T09:49:47Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Cancers |
| spelling | doaj.art-17c373df20384ebf9a8d0b84982ee3132023-11-16T16:18:34ZengMDPI AGCancers2072-66942023-01-0115388510.3390/cancers15030885Hyperparameter Optimizer with Deep Learning-Based Decision-Support Systems for Histopathological Breast Cancer DiagnosisMarwa Obayya0Mashael S. Maashi1Nadhem Nemri2Heba Mohsen3Abdelwahed Motwakel4Azza Elneil Osman5Amani A. Alneil6Mohamed Ibrahim Alsaid7Department of Biomedical Engineering, College of Engineering, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaDepartment of Software Engineering, College of Computer and Information Science, King Saud University, Riyadh 11543, Saudi ArabiaDepartment of Information Systems, College of Science & Art at Mahayil, King Khalid University, Abha 62529, Saudi ArabiaDepartment of Computer Science, Faculty of Computers and Information Technology, Future University in Egypt, New Cairo 11835, EgyptDepartment of Information Systems, College of Business Administration in Hawtat Bani Tamim, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi ArabiaDepartment of Computer and Self Development, Preparatory Year Deanship, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi ArabiaDepartment of Computer and Self Development, Preparatory Year Deanship, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi ArabiaDepartment of Computer and Self Development, Preparatory Year Deanship, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi ArabiaHistopathological images are commonly used imaging modalities for breast cancer. As manual analysis of histopathological images is difficult, automated tools utilizing artificial intelligence (AI) and deep learning (DL) methods should be modelled. The recent advancements in DL approaches will be helpful in establishing maximal image classification performance in numerous application zones. This study develops an arithmetic optimization algorithm with deep-learning-based histopathological breast cancer classification (AOADL-HBCC) technique for healthcare decision making. The AOADL-HBCC technique employs noise removal based on median filtering (MF) and a contrast enhancement process. In addition, the presented AOADL-HBCC technique applies an AOA with a SqueezeNet model to derive feature vectors. Finally, a deep belief network (DBN) classifier with an Adamax hyperparameter optimizer is applied for the breast cancer classification process. In order to exhibit the enhanced breast cancer classification results of the AOADL-HBCC methodology, this comparative study states that the AOADL-HBCC technique displays better performance than other recent methodologies, with a maximum accuracy of 96.77%.https://www.mdpi.com/2072-6694/15/3/885decision makinghealthcarebreast cancer classificationhistopathological imagesdeep learning |
| spellingShingle | Marwa Obayya Mashael S. Maashi Nadhem Nemri Heba Mohsen Abdelwahed Motwakel Azza Elneil Osman Amani A. Alneil Mohamed Ibrahim Alsaid Hyperparameter Optimizer with Deep Learning-Based Decision-Support Systems for Histopathological Breast Cancer Diagnosis Cancers decision making healthcare breast cancer classification histopathological images deep learning |
| title | Hyperparameter Optimizer with Deep Learning-Based Decision-Support Systems for Histopathological Breast Cancer Diagnosis |
| title_full | Hyperparameter Optimizer with Deep Learning-Based Decision-Support Systems for Histopathological Breast Cancer Diagnosis |
| title_fullStr | Hyperparameter Optimizer with Deep Learning-Based Decision-Support Systems for Histopathological Breast Cancer Diagnosis |
| title_full_unstemmed | Hyperparameter Optimizer with Deep Learning-Based Decision-Support Systems for Histopathological Breast Cancer Diagnosis |
| title_short | Hyperparameter Optimizer with Deep Learning-Based Decision-Support Systems for Histopathological Breast Cancer Diagnosis |
| title_sort | hyperparameter optimizer with deep learning based decision support systems for histopathological breast cancer diagnosis |
| topic | decision making healthcare breast cancer classification histopathological images deep learning |
| url | https://www.mdpi.com/2072-6694/15/3/885 |
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