An enhanced fusion of transfer learning models with optimization based clinical diagnosis of lung and colon cancer using biomedical imaging
Abstract Lung and colon cancers (LCC) are among the foremost reasons for human death and disease. Early analysis of this disorder contains various tests, namely ultrasound (US), magnetic resonance imaging (MRI), and computed tomography (CT). Despite analytical imaging, histopathology is one of the e...
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-10246-0 |
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| author | N. A. S. Vinoth J. Kalaivani R. Madonna Arieth S. Sivasakthiselvan Gi-Cheon Park Gyanendra Prasad Joshi Woong Cho |
| author_facet | N. A. S. Vinoth J. Kalaivani R. Madonna Arieth S. Sivasakthiselvan Gi-Cheon Park Gyanendra Prasad Joshi Woong Cho |
| author_sort | N. A. S. Vinoth |
| collection | DOAJ |
| description | Abstract Lung and colon cancers (LCC) are among the foremost reasons for human death and disease. Early analysis of this disorder contains various tests, namely ultrasound (US), magnetic resonance imaging (MRI), and computed tomography (CT). Despite analytical imaging, histopathology is one of the effective methods that delivers cell-level imaging of tissue under inspection. These are mainly due to a restricted number of patients receiving final analysis and early healing. Furthermore, there are probabilities of inter-observer faults. Clinical informatics is an interdisciplinary field that integrates healthcare, information technology, and data analytics to improve patient care, clinical decision-making, and medical research. Recently, deep learning (DL) proved to be effective in the medical sector, and cancer diagnosis can be made automatically by utilizing the capabilities of artificial intelligence (AI), enabling faster analysis of more cases cost-effectively. On the other hand, with extensive technical developments, DL has arisen as an effective device in medical settings, mainly in medical imaging. This study presents an Enhanced Fusion of Transfer Learning Models and Optimization-Based Clinical Biomedical Imaging for Accurate Lung and Colon Cancer Diagnosis (FTLMO-BILCCD) model. The main objective of the FTLMO-BILCCD technique is to develop an efficient method for LCC detection using clinical biomedical imaging. Initially, the image pre-processing stage applies the median filter (MF) model to eliminate the unwanted noise from the input image data. Furthermore, fusion models such as CapsNet, EffcientNetV2, and MobileNet-V3 Large are employed for the feature extraction. The FTLMO-BILCCD technique implements a hybrid of temporal pattern attention and bidirectional gated recurrent unit (TPA-BiGRU) for classification. Finally, the beluga whale optimization (BWO) technique alters the hyperparameter range of the TPA‐BiGRU model optimally and results in greater classification performance. The FTLMO-BILCCD approach is experimented with under the LCC-HI dataset. The performance validation of the FTLMO-BILCCD approach portrayed a superior accuracy value of 99.16% over existing models. |
| format | Article |
| id | doaj-art-1ab82ae1ad894a0b95b200c6a79ea0e8 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-1ab82ae1ad894a0b95b200c6a79ea0e82025-08-20T04:02:46ZengNature PortfolioScientific Reports2045-23222025-07-0115112310.1038/s41598-025-10246-0An enhanced fusion of transfer learning models with optimization based clinical diagnosis of lung and colon cancer using biomedical imagingN. A. S. Vinoth0J. Kalaivani1R. Madonna Arieth2S. Sivasakthiselvan3Gi-Cheon Park4Gyanendra Prasad Joshi5Woong Cho6Department of Computing Technologies, School of Computing, Faculty of Engineering and Technology, SRM Institute of Science and TechnologyDepartment of Computing Technologies, School of Computing, Faculty of Engineering and Technology, SRM Institute of Science and TechnologyDepartment of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and TechnologyDepartment of Computing Technologies, School of Computing, Faculty of Engineering and Technology, SRM Institute of Science and TechnologyCollege of Global AI Convergence, AI & Big Data Convergence, Seoul Christian UniversityDepartment of Electronic and AI System Engineering, Kangwon National UniversityDepartment of Electronic and AI System Engineering, Kangwon National UniversityAbstract Lung and colon cancers (LCC) are among the foremost reasons for human death and disease. Early analysis of this disorder contains various tests, namely ultrasound (US), magnetic resonance imaging (MRI), and computed tomography (CT). Despite analytical imaging, histopathology is one of the effective methods that delivers cell-level imaging of tissue under inspection. These are mainly due to a restricted number of patients receiving final analysis and early healing. Furthermore, there are probabilities of inter-observer faults. Clinical informatics is an interdisciplinary field that integrates healthcare, information technology, and data analytics to improve patient care, clinical decision-making, and medical research. Recently, deep learning (DL) proved to be effective in the medical sector, and cancer diagnosis can be made automatically by utilizing the capabilities of artificial intelligence (AI), enabling faster analysis of more cases cost-effectively. On the other hand, with extensive technical developments, DL has arisen as an effective device in medical settings, mainly in medical imaging. This study presents an Enhanced Fusion of Transfer Learning Models and Optimization-Based Clinical Biomedical Imaging for Accurate Lung and Colon Cancer Diagnosis (FTLMO-BILCCD) model. The main objective of the FTLMO-BILCCD technique is to develop an efficient method for LCC detection using clinical biomedical imaging. Initially, the image pre-processing stage applies the median filter (MF) model to eliminate the unwanted noise from the input image data. Furthermore, fusion models such as CapsNet, EffcientNetV2, and MobileNet-V3 Large are employed for the feature extraction. The FTLMO-BILCCD technique implements a hybrid of temporal pattern attention and bidirectional gated recurrent unit (TPA-BiGRU) for classification. Finally, the beluga whale optimization (BWO) technique alters the hyperparameter range of the TPA‐BiGRU model optimally and results in greater classification performance. The FTLMO-BILCCD approach is experimented with under the LCC-HI dataset. The performance validation of the FTLMO-BILCCD approach portrayed a superior accuracy value of 99.16% over existing models.https://doi.org/10.1038/s41598-025-10246-0Lung and Colon cancerMedian filterFusion of transfer learningBidirectional gated recurrent unitBeluga Whale optimization |
| spellingShingle | N. A. S. Vinoth J. Kalaivani R. Madonna Arieth S. Sivasakthiselvan Gi-Cheon Park Gyanendra Prasad Joshi Woong Cho An enhanced fusion of transfer learning models with optimization based clinical diagnosis of lung and colon cancer using biomedical imaging Scientific Reports Lung and Colon cancer Median filter Fusion of transfer learning Bidirectional gated recurrent unit Beluga Whale optimization |
| title | An enhanced fusion of transfer learning models with optimization based clinical diagnosis of lung and colon cancer using biomedical imaging |
| title_full | An enhanced fusion of transfer learning models with optimization based clinical diagnosis of lung and colon cancer using biomedical imaging |
| title_fullStr | An enhanced fusion of transfer learning models with optimization based clinical diagnosis of lung and colon cancer using biomedical imaging |
| title_full_unstemmed | An enhanced fusion of transfer learning models with optimization based clinical diagnosis of lung and colon cancer using biomedical imaging |
| title_short | An enhanced fusion of transfer learning models with optimization based clinical diagnosis of lung and colon cancer using biomedical imaging |
| title_sort | enhanced fusion of transfer learning models with optimization based clinical diagnosis of lung and colon cancer using biomedical imaging |
| topic | Lung and Colon cancer Median filter Fusion of transfer learning Bidirectional gated recurrent unit Beluga Whale optimization |
| url | https://doi.org/10.1038/s41598-025-10246-0 |
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