A Synergistic Approach to Colon Cancer Detection: Leveraging EfficientNet and NSGA-II for Enhanced Diagnostic Performance
Colon cancer remains a leading cause of cancer-related mortality globally, necessitating early and accurate diagnosis to improve patient outcomes. Traditional diagnostic methods rely heavily on manual interpretation by pathologists, which can result in inaccuracies and delays in treatment. This stud...
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2024-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10804809/ |
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| author | Noushin Saba Afia Zafar Mohsin Suleman Kainat Zafar Shahneer Zafar Adil Ali Saleem Hafeez Ur Rehman Siddiqui Muhammad Iqbal Syed Sajid Ullah |
| author_facet | Noushin Saba Afia Zafar Mohsin Suleman Kainat Zafar Shahneer Zafar Adil Ali Saleem Hafeez Ur Rehman Siddiqui Muhammad Iqbal Syed Sajid Ullah |
| author_sort | Noushin Saba |
| collection | DOAJ |
| description | Colon cancer remains a leading cause of cancer-related mortality globally, necessitating early and accurate diagnosis to improve patient outcomes. Traditional diagnostic methods rely heavily on manual interpretation by pathologists, which can result in inaccuracies and delays in treatment. This study proposes an innovative, automated approach to colon cancer diagnosis by integrating advanced machine learning techniques with deep learning architectures. We employed EfficientNet, a state-of-the-art convolutional neural network, to extract intricate features from histopathological images, alongside the Non-dominated Sorting Genetic Algorithm II for optimal feature selection. This hybrid approach significantly enhances diagnostic performance while reducing computational complexity. The model was evaluated using five diverse datasets: Colon Cancer Histopathological Images, Kvasir, Kvasir-SEG, Hyper-Kvasir, and Endotect. The results indicate that our method outperforms traditional models such as CNN, AlexNet, ResNet, and GoogleNet, achieving an accuracy of 99.97% on the Colon Cancer Histopathological Images dataset. These findings suggest that this novel approach can substantially enhance early detection and diagnosis of colon cancer, providing a scalable solution to current diagnostic challenges. Ultimately, our study lays the groundwork for future advancements in automated cancer diagnostics, contributing to improved patient outcomes and more efficient healthcare delivery. The code and dataset for reproducing these results are publicly accessible at <uri>https://github.com/Noushin-Saba/ColonCancerDetectionandDiagnosis</uri>. |
| format | Article |
| id | doaj-art-26772feb13504bccac4e466d8ad93c32 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-26772feb13504bccac4e466d8ad93c322025-08-20T01:57:04ZengIEEEIEEE Access2169-35362024-01-011219226419227810.1109/ACCESS.2024.351921610804809A Synergistic Approach to Colon Cancer Detection: Leveraging EfficientNet and NSGA-II for Enhanced Diagnostic PerformanceNoushin Saba0https://orcid.org/0009-0007-0116-8453Afia Zafar1Mohsin Suleman2https://orcid.org/0009-0008-5064-1574Kainat Zafar3Shahneer Zafar4Adil Ali Saleem5https://orcid.org/0000-0003-2468-8471Hafeez Ur Rehman Siddiqui6https://orcid.org/0000-0003-0671-2060Muhammad Iqbal7https://orcid.org/0000-0001-9587-3311Syed Sajid Ullah8https://orcid.org/0000-0002-5406-0389Department of Computer Science, National University of Technology, Islamabad, PakistanDepartment of Computer Science, National University of Technology, Islamabad, PakistanDepartment of Computer Science, National University of Technology, Islamabad, PakistanDepartment of Computer Science, National University of Technology, Islamabad, PakistanDepartment of Computer Science, National University of Technology, Islamabad, PakistanInstitute of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, Punjab, PakistanInstitute of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, Punjab, PakistanSchool of Interdisciplinary Engineering and Sciences (SINES), National University of Sciences and Technology (NUST), Islamabad, PakistanDepartment of Information and Communication Technology, University of Agder (UiA), Grimstad, NorwayColon cancer remains a leading cause of cancer-related mortality globally, necessitating early and accurate diagnosis to improve patient outcomes. Traditional diagnostic methods rely heavily on manual interpretation by pathologists, which can result in inaccuracies and delays in treatment. This study proposes an innovative, automated approach to colon cancer diagnosis by integrating advanced machine learning techniques with deep learning architectures. We employed EfficientNet, a state-of-the-art convolutional neural network, to extract intricate features from histopathological images, alongside the Non-dominated Sorting Genetic Algorithm II for optimal feature selection. This hybrid approach significantly enhances diagnostic performance while reducing computational complexity. The model was evaluated using five diverse datasets: Colon Cancer Histopathological Images, Kvasir, Kvasir-SEG, Hyper-Kvasir, and Endotect. The results indicate that our method outperforms traditional models such as CNN, AlexNet, ResNet, and GoogleNet, achieving an accuracy of 99.97% on the Colon Cancer Histopathological Images dataset. These findings suggest that this novel approach can substantially enhance early detection and diagnosis of colon cancer, providing a scalable solution to current diagnostic challenges. Ultimately, our study lays the groundwork for future advancements in automated cancer diagnostics, contributing to improved patient outcomes and more efficient healthcare delivery. The code and dataset for reproducing these results are publicly accessible at <uri>https://github.com/Noushin-Saba/ColonCancerDetectionandDiagnosis</uri>.https://ieeexplore.ieee.org/document/10804809/CNNcolon diseasecancerEfficientNetEfficientNet-NSGA-IINSGAII |
| spellingShingle | Noushin Saba Afia Zafar Mohsin Suleman Kainat Zafar Shahneer Zafar Adil Ali Saleem Hafeez Ur Rehman Siddiqui Muhammad Iqbal Syed Sajid Ullah A Synergistic Approach to Colon Cancer Detection: Leveraging EfficientNet and NSGA-II for Enhanced Diagnostic Performance IEEE Access CNN colon disease cancer EfficientNet EfficientNet-NSGA-II NSGAII |
| title | A Synergistic Approach to Colon Cancer Detection: Leveraging EfficientNet and NSGA-II for Enhanced Diagnostic Performance |
| title_full | A Synergistic Approach to Colon Cancer Detection: Leveraging EfficientNet and NSGA-II for Enhanced Diagnostic Performance |
| title_fullStr | A Synergistic Approach to Colon Cancer Detection: Leveraging EfficientNet and NSGA-II for Enhanced Diagnostic Performance |
| title_full_unstemmed | A Synergistic Approach to Colon Cancer Detection: Leveraging EfficientNet and NSGA-II for Enhanced Diagnostic Performance |
| title_short | A Synergistic Approach to Colon Cancer Detection: Leveraging EfficientNet and NSGA-II for Enhanced Diagnostic Performance |
| title_sort | synergistic approach to colon cancer detection leveraging efficientnet and nsga ii for enhanced diagnostic performance |
| topic | CNN colon disease cancer EfficientNet EfficientNet-NSGA-II NSGAII |
| url | https://ieeexplore.ieee.org/document/10804809/ |
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