Computer Aided Cervical Cancer Diagnosis Using Gazelle Optimization Algorithm With Deep Learning Model
Cervical cancer (CC), the most common cancer among women, is most commonly diagnosed through Pap smears, a crucial screening process that includes collecting cervical cells for examination. Artificial intelligence (AI)-powered computer-aided diagnoses (CAD) system becomes a promising tool for improv...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10384881/ |
| _version_ | 1827369168174841856 |
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| author | Mohamed K. Nour Imene Issaoui Alaa Edris Ahmed Mahmud Mohammed Assiri Sara Saadeldeen Ibrahim |
| author_facet | Mohamed K. Nour Imene Issaoui Alaa Edris Ahmed Mahmud Mohammed Assiri Sara Saadeldeen Ibrahim |
| author_sort | Mohamed K. Nour |
| collection | DOAJ |
| description | Cervical cancer (CC), the most common cancer among women, is most commonly diagnosed through Pap smears, a crucial screening process that includes collecting cervical cells for examination. Artificial intelligence (AI)-powered computer-aided diagnoses (CAD) system becomes a promising tool for improving CC diagnosis. Deep learning (DL), a branch of AI, holds particular potential in CAD systems for early detection and accurate diagnosis. DL algorithm is trained to identify abnormalities and patterns in Pap smear images, such as dysplasia, cellular changes, and other markers of CC. So, this study presents a Computer Aided Cervical Cancer Diagnosis utilizing the Gazelle Optimizer Algorithm with Deep Learning (CACCD-GOADL) model on Pap smear images. The foremost objective of the CACCD-GOADL approach is to examine the image detection of CC. To accomplish this, the CACCD-GOADL methodology uses an improved MobileNetv3 model for extracting complex patterns in Pap smear images. In addition, the CACCD-GOADL technique designs a new GOA for the hyperparameter tuning of the improved MobileNetv3 system. For the classification and identification of cancer, the CACCD-GOADL technique uses a stacked extreme learning machine (SELM) methodology. The simulation validation of the CACCD-GOADL approach is verified on a benchmark dataset of Herlev. Experimental results highlighted that the CACCD-GOADL algorithm reaches superior outcomes over other methods. |
| first_indexed | 2024-03-08T09:42:22Z |
| format | Article |
| id | doaj.art-0e88036431b348579b8109cee55ab816 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-08T09:42:22Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-0e88036431b348579b8109cee55ab8162024-01-30T00:02:26ZengIEEEIEEE Access2169-35362024-01-0112130461305410.1109/ACCESS.2024.335188310384881Computer Aided Cervical Cancer Diagnosis Using Gazelle Optimization Algorithm With Deep Learning ModelMohamed K. Nour0https://orcid.org/0000-0002-5768-5392Imene Issaoui1Alaa Edris2Ahmed Mahmud3Mohammed Assiri4https://orcid.org/0000-0002-6367-2977Sara Saadeldeen Ibrahim5Department of Computer Science, College of Computing and Information Systems, Umm Al-Qura University, Makkah, Saudi ArabiaUnit of Scientific Research, Applied College, Qassim University, Buraydah, Saudi ArabiaDepartment of Computer Science and Artificial Intelligence, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi ArabiaResearch Center, Future University in Egypt, New Cairo, EgyptDepartment of Computer Science, College of Sciences and Humanities—Aflaj, Prince Sattam bin Abdulaziz University, Aflaj, Saudi ArabiaDepartment of Computer Science, College of Sciences and Humanities—Aflaj, Prince Sattam bin Abdulaziz University, Aflaj, Saudi ArabiaCervical cancer (CC), the most common cancer among women, is most commonly diagnosed through Pap smears, a crucial screening process that includes collecting cervical cells for examination. Artificial intelligence (AI)-powered computer-aided diagnoses (CAD) system becomes a promising tool for improving CC diagnosis. Deep learning (DL), a branch of AI, holds particular potential in CAD systems for early detection and accurate diagnosis. DL algorithm is trained to identify abnormalities and patterns in Pap smear images, such as dysplasia, cellular changes, and other markers of CC. So, this study presents a Computer Aided Cervical Cancer Diagnosis utilizing the Gazelle Optimizer Algorithm with Deep Learning (CACCD-GOADL) model on Pap smear images. The foremost objective of the CACCD-GOADL approach is to examine the image detection of CC. To accomplish this, the CACCD-GOADL methodology uses an improved MobileNetv3 model for extracting complex patterns in Pap smear images. In addition, the CACCD-GOADL technique designs a new GOA for the hyperparameter tuning of the improved MobileNetv3 system. For the classification and identification of cancer, the CACCD-GOADL technique uses a stacked extreme learning machine (SELM) methodology. The simulation validation of the CACCD-GOADL approach is verified on a benchmark dataset of Herlev. Experimental results highlighted that the CACCD-GOADL algorithm reaches superior outcomes over other methods.https://ieeexplore.ieee.org/document/10384881/Cervical cancergazelle optimization algorithmcomputer-aided diagnosisdeep learningmachine learning |
| spellingShingle | Mohamed K. Nour Imene Issaoui Alaa Edris Ahmed Mahmud Mohammed Assiri Sara Saadeldeen Ibrahim Computer Aided Cervical Cancer Diagnosis Using Gazelle Optimization Algorithm With Deep Learning Model IEEE Access Cervical cancer gazelle optimization algorithm computer-aided diagnosis deep learning machine learning |
| title | Computer Aided Cervical Cancer Diagnosis Using Gazelle Optimization Algorithm With Deep Learning Model |
| title_full | Computer Aided Cervical Cancer Diagnosis Using Gazelle Optimization Algorithm With Deep Learning Model |
| title_fullStr | Computer Aided Cervical Cancer Diagnosis Using Gazelle Optimization Algorithm With Deep Learning Model |
| title_full_unstemmed | Computer Aided Cervical Cancer Diagnosis Using Gazelle Optimization Algorithm With Deep Learning Model |
| title_short | Computer Aided Cervical Cancer Diagnosis Using Gazelle Optimization Algorithm With Deep Learning Model |
| title_sort | computer aided cervical cancer diagnosis using gazelle optimization algorithm with deep learning model |
| topic | Cervical cancer gazelle optimization algorithm computer-aided diagnosis deep learning machine learning |
| url | https://ieeexplore.ieee.org/document/10384881/ |
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