An Empirical Analysis of Transformer-Based and Convolutional Neural Network Approaches for Early Detection and Diagnosis of Cancer Using Multimodal Imaging and Genomic Data
Early diagnosis of cancer has focused on the use of advanced algorithms to achieve accurate diagnosis. The proposed study assesses the effectiveness of Transformer-based models and Convolutional Neural Networks (CNN) in cancer diagnosis with respect to multimodal imaging and genomic data. The perfor...
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| Format: | Article |
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2025-01-01
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| author | S. K. B. Sangeetha Sandeep Kumar Mathivanan V. Muthukumaran Jaehyuk Cho and Sathishkumar Veerappampalayam Easwaramoorthy |
| author_facet | S. K. B. Sangeetha Sandeep Kumar Mathivanan V. Muthukumaran Jaehyuk Cho and Sathishkumar Veerappampalayam Easwaramoorthy |
| author_sort | S. K. B. Sangeetha |
| collection | DOAJ |
| description | Early diagnosis of cancer has focused on the use of advanced algorithms to achieve accurate diagnosis. The proposed study assesses the effectiveness of Transformer-based models and Convolutional Neural Networks (CNN) in cancer diagnosis with respect to multimodal imaging and genomic data. The performance comparisons between the two algorithmic methods with such complex datasets, which combine multi-modal imaging and genomic information, are presented. In search of the optimal neural network configuration, a series of experiments were conducted with respect to different layers, attention mechanisms in case of transformers, and convolutional architectures in case of CNNs. Besides, parameters related to training, such as learning rates, batch sizes, and optimization algorithms, have also been systematically tuned. The different models were evaluated against accuracy, precision, recall, and the F1-score. Our results show that the proposed multimodal model, with accuracy from 92.5 to 93.2, F1-scores between 91.5 and 92.2, precision of 91.5 to 92.2, and recall values of 92.5 to 93.2. In contrast, much lower accuracy, F1-scores, precision, and recall values were noticed when using baselines, especially VGG. All these findings indicate the fact that the presented techniques, especially the Multimodal and Transformer models, are more robust solutions for classification tasks with better balance between precision and recall, as well as with higher overall accuracy. This came with the cost of the expense of computational resources: CNNs are less resource-intensive but have competitive performance with better precision and recall. The results underline how algorithm selection and hyperparameter optimization play a crucial role in cancer detection tasks. This study has shown how state-of-the-art deep learning methods can be effectively combined with multi-modal data for building more accurate and efficient systems in cancer diagnosis. Two main lines of future work would be improving these algorithms and understanding their applicability in real clinical practice to obtain maximum benefits from them. |
| format | Article |
| id | doaj-art-691afbba75b841f7911617605882e08a |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-691afbba75b841f7911617605882e08a2025-01-14T00:02:24ZengIEEEIEEE Access2169-35362025-01-01136120614510.1109/ACCESS.2024.352456410819353An Empirical Analysis of Transformer-Based and Convolutional Neural Network Approaches for Early Detection and Diagnosis of Cancer Using Multimodal Imaging and Genomic DataS. K. B. Sangeetha0https://orcid.org/0000-0002-6927-6916Sandeep Kumar Mathivanan1https://orcid.org/0000-0001-8572-1197V. Muthukumaran2Jaehyuk Cho3https://orcid.org/0000-0002-9113-6805and Sathishkumar Veerappampalayam Easwaramoorthy4https://orcid.org/0000-0002-8271-2022Department of Computer Science and Engineering, SRM Institute of Science and Technology, Vadapalani Campus, Chennai, Tamil Nadu, IndiaSchool of Computer Science and Engineering, Galgotias University, Greater Noida, IndiaDepartment of Mathematics, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Chennai, IndiaDepartment of Software Engineering and Division of Electronics and Information Engineering, Jeonbuk National University, Jeonju-si, Republic of KoreaSchool of Engineering and Technology, Sunway University, Petaling Jaya, Selangor Darul Ehsan, MalaysiaEarly diagnosis of cancer has focused on the use of advanced algorithms to achieve accurate diagnosis. The proposed study assesses the effectiveness of Transformer-based models and Convolutional Neural Networks (CNN) in cancer diagnosis with respect to multimodal imaging and genomic data. The performance comparisons between the two algorithmic methods with such complex datasets, which combine multi-modal imaging and genomic information, are presented. In search of the optimal neural network configuration, a series of experiments were conducted with respect to different layers, attention mechanisms in case of transformers, and convolutional architectures in case of CNNs. Besides, parameters related to training, such as learning rates, batch sizes, and optimization algorithms, have also been systematically tuned. The different models were evaluated against accuracy, precision, recall, and the F1-score. Our results show that the proposed multimodal model, with accuracy from 92.5 to 93.2, F1-scores between 91.5 and 92.2, precision of 91.5 to 92.2, and recall values of 92.5 to 93.2. In contrast, much lower accuracy, F1-scores, precision, and recall values were noticed when using baselines, especially VGG. All these findings indicate the fact that the presented techniques, especially the Multimodal and Transformer models, are more robust solutions for classification tasks with better balance between precision and recall, as well as with higher overall accuracy. This came with the cost of the expense of computational resources: CNNs are less resource-intensive but have competitive performance with better precision and recall. The results underline how algorithm selection and hyperparameter optimization play a crucial role in cancer detection tasks. This study has shown how state-of-the-art deep learning methods can be effectively combined with multi-modal data for building more accurate and efficient systems in cancer diagnosis. Two main lines of future work would be improving these algorithms and understanding their applicability in real clinical practice to obtain maximum benefits from them.https://ieeexplore.ieee.org/document/10819353/Transformer-based modelsconvolutional neural networks (CNNs)cancer detectionmultimodal imaginggenomic data analysis |
| spellingShingle | S. K. B. Sangeetha Sandeep Kumar Mathivanan V. Muthukumaran Jaehyuk Cho and Sathishkumar Veerappampalayam Easwaramoorthy An Empirical Analysis of Transformer-Based and Convolutional Neural Network Approaches for Early Detection and Diagnosis of Cancer Using Multimodal Imaging and Genomic Data IEEE Access Transformer-based models convolutional neural networks (CNNs) cancer detection multimodal imaging genomic data analysis |
| title | An Empirical Analysis of Transformer-Based and Convolutional Neural Network Approaches for Early Detection and Diagnosis of Cancer Using Multimodal Imaging and Genomic Data |
| title_full | An Empirical Analysis of Transformer-Based and Convolutional Neural Network Approaches for Early Detection and Diagnosis of Cancer Using Multimodal Imaging and Genomic Data |
| title_fullStr | An Empirical Analysis of Transformer-Based and Convolutional Neural Network Approaches for Early Detection and Diagnosis of Cancer Using Multimodal Imaging and Genomic Data |
| title_full_unstemmed | An Empirical Analysis of Transformer-Based and Convolutional Neural Network Approaches for Early Detection and Diagnosis of Cancer Using Multimodal Imaging and Genomic Data |
| title_short | An Empirical Analysis of Transformer-Based and Convolutional Neural Network Approaches for Early Detection and Diagnosis of Cancer Using Multimodal Imaging and Genomic Data |
| title_sort | empirical analysis of transformer based and convolutional neural network approaches for early detection and diagnosis of cancer using multimodal imaging and genomic data |
| topic | Transformer-based models convolutional neural networks (CNNs) cancer detection multimodal imaging genomic data analysis |
| url | https://ieeexplore.ieee.org/document/10819353/ |
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