A Deep Learning Algorithm for Lung Cancer Detection Using EfficientNet-B3
Lung carcinoma is one of the main causes of deaths over the whole world, causing a global burden of morbidity and mortality. Detecting lung tumors at their early stages can help reducing the risk of having lung cancer. This paper proposes a deep learning algorithm using EfficientNet B3 for lung can...
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| Format: | Article |
| Language: | English |
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College of Computer and Information Technology – University of Wasit, Iraq
2023-12-01
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| Series: | Wasit Journal of Computer and Mathematics Science |
| Subjects: | |
| Online Access: | https://wjcm.uowasit.edu.iq/index.php/wjcm/article/view/209 |
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| author | Ahmed Adil Nafea Mohammed Salah Ibrahim Mustafa Muslih Shwaysh Kibriya Abdul-Kadhim Hiba Rashid Almamoori Mohammed M AL-Ani |
| author_facet | Ahmed Adil Nafea Mohammed Salah Ibrahim Mustafa Muslih Shwaysh Kibriya Abdul-Kadhim Hiba Rashid Almamoori Mohammed M AL-Ani |
| author_sort | Ahmed Adil Nafea |
| collection | DOAJ |
| description |
Lung carcinoma is one of the main causes of deaths over the whole world, causing a global burden of morbidity and mortality. Detecting lung tumors at their early stages can help reducing the risk of having lung cancer. This paper proposes a deep learning algorithm using EfficientNet B3 for lung cancer detection. The purpose is to improve detection accuracy highlighting potential to revolutionize the field of medical imaging and improve patient care. The proposed approach is build based on EfficientNet B3 model to classify four different types of lung cancer. The approach used CT scan images labeled into Normal, Squamous.cell.carcinoma, Large.cell.carcinoma, and Adenocarcinoma for the purpose of lung cancer detection. The results showed that the proposed model provided an improvement rate of 2.13% compared with the best-trained classifier with accuracy of 96%. This model can be generalized to improve lung cancer detection. The finding of deep neural networks, particularly EfficientNet B3, in supporting the diagnosis and detection of the lung disease, particularly in its early times.
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| first_indexed | 2024-03-07T19:03:06Z |
| format | Article |
| id | doaj.art-1fd40e90e7634cb681b823edbe7dc36a |
| institution | Directory Open Access Journal |
| issn | 2788-5879 2788-5887 |
| language | English |
| last_indexed | 2024-04-24T07:07:16Z |
| publishDate | 2023-12-01 |
| publisher | College of Computer and Information Technology – University of Wasit, Iraq |
| record_format | Article |
| series | Wasit Journal of Computer and Mathematics Science |
| spelling | doaj.art-1fd40e90e7634cb681b823edbe7dc36a2024-04-21T17:26:51ZengCollege of Computer and Information Technology – University of Wasit, IraqWasit Journal of Computer and Mathematics Science2788-58792788-58872023-12-012410.31185/wjcms.209A Deep Learning Algorithm for Lung Cancer Detection Using EfficientNet-B3Ahmed Adil Nafea0Mohammed Salah Ibrahim1Mustafa Muslih Shwaysh2Kibriya Abdul-Kadhim3Hiba Rashid Almamoori4Mohammed M AL-Ani5Department of Artificial Intelligence, College of Computer Science and IT, University of Anbar, Ramadi, IraqDepartment of Artificial Intelligence, College of Computer Science and IT, University of Anbar, Ramadi, IraqCollege of Education for Humanities, University of Anbar, Ramadi, IraqDepartment of Artificial Intelligence, College of Computer Science and IT, University of Anbar, Ramadi, IraqDepartment of Information Networks, College of Information Technology, University of Babylon, IraqCenter for Artificial Intelligence Technology (CAIT), Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi, Selangor, Malaysia Lung carcinoma is one of the main causes of deaths over the whole world, causing a global burden of morbidity and mortality. Detecting lung tumors at their early stages can help reducing the risk of having lung cancer. This paper proposes a deep learning algorithm using EfficientNet B3 for lung cancer detection. The purpose is to improve detection accuracy highlighting potential to revolutionize the field of medical imaging and improve patient care. The proposed approach is build based on EfficientNet B3 model to classify four different types of lung cancer. The approach used CT scan images labeled into Normal, Squamous.cell.carcinoma, Large.cell.carcinoma, and Adenocarcinoma for the purpose of lung cancer detection. The results showed that the proposed model provided an improvement rate of 2.13% compared with the best-trained classifier with accuracy of 96%. This model can be generalized to improve lung cancer detection. The finding of deep neural networks, particularly EfficientNet B3, in supporting the diagnosis and detection of the lung disease, particularly in its early times. https://wjcm.uowasit.edu.iq/index.php/wjcm/article/view/209Classification DetectionDeep learningEfficientNetLung cancer |
| spellingShingle | Ahmed Adil Nafea Mohammed Salah Ibrahim Mustafa Muslih Shwaysh Kibriya Abdul-Kadhim Hiba Rashid Almamoori Mohammed M AL-Ani A Deep Learning Algorithm for Lung Cancer Detection Using EfficientNet-B3 Wasit Journal of Computer and Mathematics Science Classification Detection Deep learning EfficientNet Lung cancer |
| title | A Deep Learning Algorithm for Lung Cancer Detection Using EfficientNet-B3 |
| title_full | A Deep Learning Algorithm for Lung Cancer Detection Using EfficientNet-B3 |
| title_fullStr | A Deep Learning Algorithm for Lung Cancer Detection Using EfficientNet-B3 |
| title_full_unstemmed | A Deep Learning Algorithm for Lung Cancer Detection Using EfficientNet-B3 |
| title_short | A Deep Learning Algorithm for Lung Cancer Detection Using EfficientNet-B3 |
| title_sort | deep learning algorithm for lung cancer detection using efficientnet b3 |
| topic | Classification Detection Deep learning EfficientNet Lung cancer |
| url | https://wjcm.uowasit.edu.iq/index.php/wjcm/article/view/209 |
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