Publication Type : Conference Proceedings
Publisher : ACM
Source : Proceedings of the 2024 8th International Conference on Algorithms, Computing and Systems
Url : https://doi.org/10.1145/3708597.3708613
Campus : Nagercoil
School : School of Computing
Year : 2024
Abstract : In this research, the performance of different deep learning models, including CNN + RNN, VGG16 + RNN, VGG19 + RNN, ResNet50 + RNN, and InceptionV3 + RNN in predicting brain tumour grades by employing Electronic Health Record (EHR) and imaging data are evaluated. Confusion matrices show the models' abilities to predict, showing the results of the prediction capability of each model over tumour grades. From the result and analysis, InceptionV3+RNN is performing better than all other ML models. With the multimodal fusion techniques, an informed decision can be made about the grades of the cancer cells, and the clinicians can give the proper treatment to the patients. The proposed approach can be implemented in real-time to predict the cancer grades with the highest level of accuracy for better treatment.
Cite this Research Publication : Siva Raja P M, Vidhya S, Sumithra R P, Anjana S, Rejini K, Ramanan K, Advances in Multimodal Fusion of EHR and Medical Imaging Data Using deep learning techniques for advanced treatment of brain cancer, Proceedings of the 2024 8th International Conference on Algorithms, Computing and Systems, ACM, 2024, https://doi.org/10.1145/3708597.3708613