Hybrid Metaheuristics With Deep Learning-Based Fusion Model for Biomedical Image Analysis
Biomedical image analysis has played a pivotal role in modern healthcare by facilitating automated analysis and interpretation of medical images. Biomedical image classification is the process of automatically labelling or categorizing medical images based on their content. In recent years, this fie...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10288439/ |
| _version_ | 1797648660589182976 |
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| author | Marwa Obayya Muhammad Kashif Saeed Nuha Alruwais Saud S. Alotaibi Mohammed Assiri Ahmed S. Salama |
| author_facet | Marwa Obayya Muhammad Kashif Saeed Nuha Alruwais Saud S. Alotaibi Mohammed Assiri Ahmed S. Salama |
| author_sort | Marwa Obayya |
| collection | DOAJ |
| description | Biomedical image analysis has played a pivotal role in modern healthcare by facilitating automated analysis and interpretation of medical images. Biomedical image classification is the process of automatically labelling or categorizing medical images based on their content. In recent years, this field has received considerable attention because of the abundance of bio-medical image data and the potential for deep learning (DL) algorithms to assist medical staff in identifying diseases and making treatment decisions. DL methods are mostly convolutional neural networks (CNN) has illustrated outstanding performance in analyzing and classifying biomedical images. Therefore, this study presents a new Hybrid Metaheuristics with Deep Learning based Fusion Model Biomedical Image Analysis (HMDL-MFMBIA) technique. The HMDL-MFMBIA technique initially performs image pre-processing and Swin-UNet-based segmentation. Besides, a fusion of multiple DL-based feature extractors takes place using Xception and Residual Network (ResNet) model. Moreover, a hybrid salp swarm algorithm (HSSA) was employed for the optimal hyperparameter selection of the DL models. Finally, the gated recurrent unit (GRU) algorithm can be exploited for the detection and classification of bio-medical images. A widespread of simulated is conducted to establish the enhanced biomedical image classification results of the HMDL-MFMBIA method. The simulation outcomes inferred the greater outcome of the HMDL-MFMBIA algorithm over other DL models. |
| first_indexed | 2024-03-11T15:35:08Z |
| format | Article |
| id | doaj.art-752b9f09da9b4f938b58e50aec0aa2fd |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-11T15:35:08Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-752b9f09da9b4f938b58e50aec0aa2fd2023-10-26T23:01:11ZengIEEEIEEE Access2169-35362023-01-011111714911715810.1109/ACCESS.2023.332636910288439Hybrid Metaheuristics With Deep Learning-Based Fusion Model for Biomedical Image AnalysisMarwa Obayya0https://orcid.org/0000-0003-3099-9567Muhammad Kashif Saeed1https://orcid.org/0000-0002-9300-2157Nuha Alruwais2https://orcid.org/0009-0009-0119-869XSaud S. Alotaibi3https://orcid.org/0000-0003-1082-513XMohammed Assiri4https://orcid.org/0000-0002-6367-2977Ahmed S. Salama5Department of Biomedical Engineering, College of Engineering, Princess Nourah bint Abdulrahman University, Riyadh, Saudi ArabiaDepartment of Computer Science, Applied College at Muhayil, King Khalid University, Abha, Saudi ArabiaDepartment of Computer Science and Engineering, College of Applied Studies and Community Services, King Saud University, Riyadh, Saudi ArabiaDepartment of Information Systems, College of Computing and Information System, Umm Al-Qura University, Makkah, Saudi ArabiaDepartment of Computer Science, College of Sciences and Humanities-Aflaj, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi ArabiaDepartment of Electrical Engineering, Faculty of Engineering and Technology, Future University in Egypt, New Cairo, EgyptBiomedical image analysis has played a pivotal role in modern healthcare by facilitating automated analysis and interpretation of medical images. Biomedical image classification is the process of automatically labelling or categorizing medical images based on their content. In recent years, this field has received considerable attention because of the abundance of bio-medical image data and the potential for deep learning (DL) algorithms to assist medical staff in identifying diseases and making treatment decisions. DL methods are mostly convolutional neural networks (CNN) has illustrated outstanding performance in analyzing and classifying biomedical images. Therefore, this study presents a new Hybrid Metaheuristics with Deep Learning based Fusion Model Biomedical Image Analysis (HMDL-MFMBIA) technique. The HMDL-MFMBIA technique initially performs image pre-processing and Swin-UNet-based segmentation. Besides, a fusion of multiple DL-based feature extractors takes place using Xception and Residual Network (ResNet) model. Moreover, a hybrid salp swarm algorithm (HSSA) was employed for the optimal hyperparameter selection of the DL models. Finally, the gated recurrent unit (GRU) algorithm can be exploited for the detection and classification of bio-medical images. A widespread of simulated is conducted to establish the enhanced biomedical image classification results of the HMDL-MFMBIA method. The simulation outcomes inferred the greater outcome of the HMDL-MFMBIA algorithm over other DL models.https://ieeexplore.ieee.org/document/10288439/Biomedical image analysisimage classificationcomputer visionfusion modeldeep learning |
| spellingShingle | Marwa Obayya Muhammad Kashif Saeed Nuha Alruwais Saud S. Alotaibi Mohammed Assiri Ahmed S. Salama Hybrid Metaheuristics With Deep Learning-Based Fusion Model for Biomedical Image Analysis IEEE Access Biomedical image analysis image classification computer vision fusion model deep learning |
| title | Hybrid Metaheuristics With Deep Learning-Based Fusion Model for Biomedical Image Analysis |
| title_full | Hybrid Metaheuristics With Deep Learning-Based Fusion Model for Biomedical Image Analysis |
| title_fullStr | Hybrid Metaheuristics With Deep Learning-Based Fusion Model for Biomedical Image Analysis |
| title_full_unstemmed | Hybrid Metaheuristics With Deep Learning-Based Fusion Model for Biomedical Image Analysis |
| title_short | Hybrid Metaheuristics With Deep Learning-Based Fusion Model for Biomedical Image Analysis |
| title_sort | hybrid metaheuristics with deep learning based fusion model for biomedical image analysis |
| topic | Biomedical image analysis image classification computer vision fusion model deep learning |
| url | https://ieeexplore.ieee.org/document/10288439/ |
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