A Deep Learning Approach for Automated Diagnosis and Multi-Class Classification of Alzheimer's Disease Stages Using Resting-State fMRI and Residual Neural Networks
| Autor: | Tanzila Saba, Sajid Iqbal, Asim Rehmat, Zahid Mehmood, Muhammad Usman Ghani Khan, Amjad Rehman, Farheen Ramzan |
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| Rok vydání: | 2019 |
| Předmět: |
020205 medical informatics
Rest Medicine (miscellaneous) Health Informatics 02 engineering and technology Machine learning computer.software_genre Multiclass classification Deep Learning Health Information Management Neuroimaging Alzheimer Disease Image Interpretation Computer-Assisted Neural Pathways 0202 electrical engineering electronic engineering information engineering medicine Dementia Humans Diagnosis Computer-Assisted medicine.diagnostic_test Resting state fMRI Receiver operating characteristic business.industry Deep learning Brain medicine.disease Magnetic Resonance Imaging Binary classification Artificial intelligence Neural Networks Computer Functional magnetic resonance imaging business computer Information Systems |
| Zdroj: | Journal of medical systems. 44(2) |
| ISSN: | 1573-689X |
| Popis: | Alzheimer’s disease (AD) is an incurable neurodegenerative disorder accounting for 70%–80% dementia cases worldwide. Although, research on AD has increased in recent years, however, the complexity associated with brain structure and functions makes the early diagnosis of this disease a challenging task. Resting-state functional magnetic resonance imaging (rs-fMRI) is a neuroimaging technology that has been widely used to study the pathogenesis of neurodegenerative diseases. In literature, the computer-aided diagnosis of AD is limited to binary classification or diagnosis of AD and MCI stages. However, its applicability to diagnose multiple progressive stages of AD is relatively under-studied. This study explores the effectiveness of rs-fMRI for multi-class classification of AD and its associated stages including CN, SMC, EMCI, MCI, LMCI, and AD. A longitudinal cohort of resting-state fMRI of 138 subjects (25 CN, 25 SMC, 25 EMCI, 25 LMCI, 13 MCI, and 25 AD) from Alzheimer’s Disease Neuroimaging Initiative (ADNI) is studied. To provide a better insight into deep learning approaches and their applications to AD classification, we investigate ResNet-18 architecture in detail. We consider the training of the network from scratch by using single-channel input as well as performed transfer learning with and without fine-tuning using an extended network architecture. We experimented with residual neural networks to perform AD classification task and compared it with former research in this domain. The performance of the models is evaluated using precision, recall, f1-measure, AUC and ROC curves. We found that our networks were able to significantly classify the subjects. We achieved improved results with our fine-tuned model for all the AD stages with an accuracy of 100%, 96.85%, 97.38%, 97.43%, 97.40% and 98.01% for CN, SMC, EMCI, LMCI, MCI, and AD respectively. However, in terms of overall performance, we achieved state-of-the-art results with an average accuracy of 97.92% and 97.88% for off-the-shelf and fine-tuned models respectively. The Analysis of results indicate that classification and prediction of neurodegenerative brain disorders such as AD using functional magnetic resonance imaging and advanced deep learning methods is promising for clinical decision making and have the potential to assist in early diagnosis of AD and its associated stages. |
| Databáze: | OpenAIRE |
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