A Novel Deep Learning Approach for Retinopathy Prediction Using Multimodal Data Fusion
- Title
- A Novel Deep Learning Approach for Retinopathy Prediction Using Multimodal Data Fusion
- Creator
- Kumar A.; Sharma N.Kr.; Reshmi B.; Saini R.; Thanganadar H.; Singhal A.; Rao P.V.
- Description
- In contemporary research on mild cognitive disorders (MCI) and Alzheimer's disease (AD), the predominant approach involves the utilization of double data modalities for making predictions related to AD stages. However, there is a growing recognition of the potential benefits that could be derived from the fusion of multiple data modalities to obtain a more comprehensive perspective in the analysis of AD staging. To address this, we have employed deep learning techniques to holistically assess data from various sources, including, genetic (single nucleotide polymorphisms (SNPs)), imaging (magnetic resonance imaging (MRI)), and clinical tests, with the objective of categorizing patients into distinct groups: AD, MCI, and controls (CN). For the analysis of imaging data, convolutional neural networks have been employed. Moreover, we have introduced a novel approach for data interpretation, enabling the identification of the most influential features learned by these deep models. This interpretation process incorporates clustering and perturbation analysis, shedding light on the crucial aspects of the data contributing to our classification results. Our experimentation, conducted on the dataset (i.e., ADNI), has yielded compelling results. Furthermore, our findings have underscored the significant advantage of integrating multi-modality data over solely relying on double modality models, as it has led to improvements in terms of accuracy, precision, recall, and mean F1 scores. 2024, Ismail Saritas. All rights reserved.
- Source
- International Journal of Intelligent Systems and Applications in Engineering, Vol-12, No. 11s, pp. 70-77.
- Date
- 2024-01-01
- Publisher
- Ismail Saritas
- Subject
- Clustering; Deep Learning; Multimodal; Retinopathy
- Coverage
- Kumar A., School of Computing Science & Engineering, Galgotias University, India; Sharma N.Kr., Department of MCA, IIMT College of Engineering, Gr.Noida, Dr. A.P.J. Abdul Kalam Technical University, UP, Lucknow, India; Reshmi B., Department of Computer Science and Engineering, Ahalia School of Engineering and Technology, Kerala, Palakkad, India; Saini R., Department of Computer Science and Engineering, G. B. Pant Institute of Engineering and Technology, Pauri Garhwal, Uttarakhand, India; Thanganadar H., Department of Health Informatics, College of Public Health and Tropical Medicine, Jazan University, Jazan, 45142, Saudi Arabia; Singhal A., School of Sciences, Christ (Deemed to be University) Delhi-NCR, Uttar Pradesh, Ghaziabad, 201003, India; Rao P.V., Gokaraju Rangaraju Institute of Engineering and Technology(GRIET), Bachupally, Hyderabad, 500090, India
- Rights
- Restricted Access
- Relation
- ISSN: 21476799
- Format
- Online
- Language
- English
- Type
- Article
Collection
Citation
Kumar A.; Sharma N.Kr.; Reshmi B.; Saini R.; Thanganadar H.; Singhal A.; Rao P.V., “A Novel Deep Learning Approach for Retinopathy Prediction Using Multimodal Data Fusion,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 27, 2025, https://archives.christuniversity.in/items/show/13354.