Dung Beetle Optimization with Deep Feature Fusion Model for Lung Cancer Detection and Classification
Lung cancer is the main cause of cancer deaths all over the world. An important reason for these deaths was late analysis and worse prediction. With the accelerated improvement of deep learning (DL) approaches, DL can be effectively and widely executed for several real-world applications in healthca...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Cancers |
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| Online Access: | https://www.mdpi.com/2072-6694/15/15/3982 |
| _version_ | 1797586964990394368 |
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| author | Mohammad Alamgeer Nuha Alruwais Haya Mesfer Alshahrani Abdullah Mohamed Mohammed Assiri |
| author_facet | Mohammad Alamgeer Nuha Alruwais Haya Mesfer Alshahrani Abdullah Mohamed Mohammed Assiri |
| author_sort | Mohammad Alamgeer |
| collection | DOAJ |
| description | Lung cancer is the main cause of cancer deaths all over the world. An important reason for these deaths was late analysis and worse prediction. With the accelerated improvement of deep learning (DL) approaches, DL can be effectively and widely executed for several real-world applications in healthcare systems, like medical image interpretation and disease analysis. Medical imaging devices can be vital in primary-stage lung tumor analysis and the observation of lung tumors from the treatment. Many medical imaging modalities like computed tomography (CT), chest X-ray (CXR), molecular imaging, magnetic resonance imaging (MRI), and positron emission tomography (PET) systems are widely analyzed for lung cancer detection. This article presents a new dung beetle optimization modified deep feature fusion model for lung cancer detection and classification (DBOMDFF-LCC) technique. The presented DBOMDFF-LCC technique mainly depends upon the feature fusion and hyperparameter tuning process. To accomplish this, the DBOMDFF-LCC technique uses a feature fusion process comprising three DL models, namely residual network (ResNet), densely connected network (DenseNet), and Inception-ResNet-v2. Furthermore, the DBO approach was employed for the optimum hyperparameter selection of three DL approaches. For lung cancer detection purposes, the DBOMDFF-LCC system utilizes a long short-term memory (LSTM) approach. The simulation result analysis of the DBOMDFF-LCC technique of the medical dataset is investigated using different evaluation metrics. The extensive comparative results highlighted the betterment of the DBOMDFF-LCC technique of lung cancer classification. |
| first_indexed | 2024-03-11T00:30:37Z |
| format | Article |
| id | doaj.art-a97eef01125845e397ee2767075eaeea |
| institution | Directory Open Access Journal |
| issn | 2072-6694 |
| language | English |
| last_indexed | 2024-03-11T00:30:37Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Cancers |
| spelling | doaj.art-a97eef01125845e397ee2767075eaeea2023-11-18T22:44:03ZengMDPI AGCancers2072-66942023-08-011515398210.3390/cancers15153982Dung Beetle Optimization with Deep Feature Fusion Model for Lung Cancer Detection and ClassificationMohammad Alamgeer0Nuha Alruwais1Haya Mesfer Alshahrani2Abdullah Mohamed3Mohammed Assiri4Department of Information Systems, College of Science & Art at Mahayil, King Khalid University, Abha 61421, Saudi ArabiaDepartment of Computer Science and Engineering, College of Applied Studies and Community Services, King Saud University, P.O. Box 22459, Riyadh 11495, Saudi ArabiaDepartment of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaResearch Centre, Future University in Egypt, New Cairo 11845, EgyptDepartment of Computer Science, College of Sciences and Humanities-Aflaj, Prince Sattam bin Abdulaziz University, Aflaj 16273, Saudi ArabiaLung cancer is the main cause of cancer deaths all over the world. An important reason for these deaths was late analysis and worse prediction. With the accelerated improvement of deep learning (DL) approaches, DL can be effectively and widely executed for several real-world applications in healthcare systems, like medical image interpretation and disease analysis. Medical imaging devices can be vital in primary-stage lung tumor analysis and the observation of lung tumors from the treatment. Many medical imaging modalities like computed tomography (CT), chest X-ray (CXR), molecular imaging, magnetic resonance imaging (MRI), and positron emission tomography (PET) systems are widely analyzed for lung cancer detection. This article presents a new dung beetle optimization modified deep feature fusion model for lung cancer detection and classification (DBOMDFF-LCC) technique. The presented DBOMDFF-LCC technique mainly depends upon the feature fusion and hyperparameter tuning process. To accomplish this, the DBOMDFF-LCC technique uses a feature fusion process comprising three DL models, namely residual network (ResNet), densely connected network (DenseNet), and Inception-ResNet-v2. Furthermore, the DBO approach was employed for the optimum hyperparameter selection of three DL approaches. For lung cancer detection purposes, the DBOMDFF-LCC system utilizes a long short-term memory (LSTM) approach. The simulation result analysis of the DBOMDFF-LCC technique of the medical dataset is investigated using different evaluation metrics. The extensive comparative results highlighted the betterment of the DBOMDFF-LCC technique of lung cancer classification.https://www.mdpi.com/2072-6694/15/15/3982lung cancerdeep learningfeature fusion modeldung beetle optimizercomputer-aided diagnosis |
| spellingShingle | Mohammad Alamgeer Nuha Alruwais Haya Mesfer Alshahrani Abdullah Mohamed Mohammed Assiri Dung Beetle Optimization with Deep Feature Fusion Model for Lung Cancer Detection and Classification Cancers lung cancer deep learning feature fusion model dung beetle optimizer computer-aided diagnosis |
| title | Dung Beetle Optimization with Deep Feature Fusion Model for Lung Cancer Detection and Classification |
| title_full | Dung Beetle Optimization with Deep Feature Fusion Model for Lung Cancer Detection and Classification |
| title_fullStr | Dung Beetle Optimization with Deep Feature Fusion Model for Lung Cancer Detection and Classification |
| title_full_unstemmed | Dung Beetle Optimization with Deep Feature Fusion Model for Lung Cancer Detection and Classification |
| title_short | Dung Beetle Optimization with Deep Feature Fusion Model for Lung Cancer Detection and Classification |
| title_sort | dung beetle optimization with deep feature fusion model for lung cancer detection and classification |
| topic | lung cancer deep learning feature fusion model dung beetle optimizer computer-aided diagnosis |
| url | https://www.mdpi.com/2072-6694/15/15/3982 |
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