Diagnosis of brain diseases in fusion of neuroimaging modalities using deep learning: A review
Article
Shoeibi, Afshin, Khodatars, Marjane, Jafari, Mahboobeh, Ghassemi, Navid, Moridian, Parisa, Alizadehsani, Roohallah, Ling, Sai Ho, Khosravi, Abbas, Alinejad-Rokny, Hamid, Lam, H.K., Fuller-Tyszkiewicz, Matthew D., Acharya, U. Rajendra, Anderson, Donovan, Zhang, Yudong and Gorriz, Juan Manuel. 2023. "Diagnosis of brain diseases in fusion of neuroimaging modalities using deep learning: A review." Information Fusion. 93, pp. 85-117. https://doi.org/10.1016/j.inffus.2022.12.010
| Article Title | Diagnosis of brain diseases in fusion of neuroimaging modalities using deep learning: A review |
|---|---|
| ERA Journal ID | 20983 |
| Article Category | Article |
| Authors | Shoeibi, Afshin, Khodatars, Marjane, Jafari, Mahboobeh, Ghassemi, Navid, Moridian, Parisa, Alizadehsani, Roohallah, Ling, Sai Ho, Khosravi, Abbas, Alinejad-Rokny, Hamid, Lam, H.K., Fuller-Tyszkiewicz, Matthew D., Acharya, U. Rajendra, Anderson, Donovan, Zhang, Yudong and Gorriz, Juan Manuel |
| Journal Title | Information Fusion |
| Journal Citation | 93, pp. 85-117 |
| Number of Pages | 33 |
| Year | 2023 |
| Publisher | Elsevier |
| Place of Publication | Netherlands |
| ISSN | 1566-2535 |
| 1872-6305 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1016/j.inffus.2022.12.010 |
| Web Address (URL) | https://www.sciencedirect.com/science/article/abs/pii/S1566253522002573 |
| Abstract | Brain diseases, including tumors and mental and neurological disorders, seriously threaten the health and well-being of millions of people worldwide. Structural and functional neuroimaging modalities are commonly used by physicians to aid the diagnosis of brain diseases. In clinical settings, specialist doctors typically fuse the magnetic resonance imaging (MRI) data with other neuroimaging modalities for brain disease detection. As these two approaches offer complementary information, fusing these neuroimaging modalities helps physicians accurately diagnose brain diseases. Typically, fusion is performed between a functional and a structural neuroimaging modality. Because the functional modality can complement the structural modality information, thus improving the performance for the diagnosis of brain diseases by specialists. However, analyzing the fusion of neuroimaging modalities is difficult for specialist doctors. Deep Learning (DL) is a branch of artificial intelligence that has shown superior performances compared to more conventional methods in tasks such as brain disease detection from neuroimaging modalities. This work presents a comprehensive review paper in the field of brain disease detection from the fusion of neuroimaging modalities using DL models like convolutional neural networks (CNNs), recurrent neural networks (RNNs), pretrained, generative adversarial networks (GANs), and Autoencoders (AEs). First, neuroimaging modalities and the need for fusion are discussed. Then, review papers published in the field of neuroimaging multimodalities using AI techniques are explored. Moreover, fusion levels based on DL methods, including input, layer, and decision, with related studies conducted on diagnosing brain diseases, are discussed. Other sections present the most important challenges for diagnosing brain diseases from the fusion of neuroimaging modalities. In the discussion section, the details of previous research on the fusion of neuroimaging modalities based on MRI and DL models are reported. In the following, the most important future directions include Datasets, DA, imbalanced data, DL models, explainable AI, and hardware resources are presented. Finally, the main findings of this study are presented in the conclusion section. |
| Keywords | Brain diseases; MRI; Neuroimaging; Fusion; Multimodality; Deep learning |
| ANZSRC Field of Research 2020 | 400306. Computational physiology |
| Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
| Byline Affiliations | University of Granada, Spain |
| University of New South Wales | |
| Islamic Azad University, Iran | |
| Deakin University | |
| University of Technology Sydney | |
| Macquarie University | |
| King's College London, United Kingdom | |
| Ngee Ann Polytechnic, Singapore | |
| Asia University, Taiwan | |
| Singapore University of Social Sciences (SUSS), Singapore | |
| University of Leicester, United Kingdom | |
| University of Cambridge, United Kingdom |
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