A deep learning approach for diagnosing schizophrenic patients

Srinivasagopalan, Srivathsan; Barry, Justin; Gurupur, Varadraj P.; Thankachan, Sharma V.

doi:10.1080/0952813x.2018.1563636

Cited by 43 publications

(25 citation statements)

References 27 publications

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“…Qi and Tejedor ( 2016 ) used deep canonical correlation analysis (DCCA) and deep canonically correlated auto-encoders (DCCAE) to fuse multi-modality features. But in the proposed method, two modalities features directly were combined as 411 dimensional vector, then fed to the three-layer DNN model (Srinivasagopalan et al, 2019 ). To alleviate the missing modality, the synthetic sMRI and rs-fMRI images were generated by a generator proposed, and they were then used to train a multi-modality DNN (Ulloa et al, 2018 ).…”

Section: Applications In Brain Disorder Analysis With Medical Imagmentioning

confidence: 99%

A Survey on Deep Learning for Neuroimaging-Based Brain Disorder Analysis

et al. 2020

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Deep learning has recently been used for the analysis of neuroimages, such as structural magnetic resonance imaging (MRI), functional MRI, and positron emission tomography (PET), and it has achieved significant performance improvements over traditional machine learning in computer-aided diagnosis of brain disorders. This paper reviews the applications of deep learning methods for neuroimaging-based brain disorder analysis. We first provide a comprehensive overview of deep learning techniques and popular network architectures by introducing various types of deep neural networks and recent developments. We then review deep learning methods for computer-aided analysis of four typical brain disorders, including Alzheimer's disease, Parkinson's disease, Autism spectrum disorder, and Schizophrenia, where the first two diseases are neurodegenerative disorders and the last two are neurodevelopmental and psychiatric disorders, respectively. More importantly, we discuss the limitations of existing studies and present possible future directions.

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Section: Applications In Brain Disorder Analysis With Medical Imagmentioning

confidence: 99%

A Survey on Deep Learning for Neuroimaging-Based Brain Disorder Analysis

et al. 2020

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“…Highest accuracy 98.09% of schizophrenia detection has been observed in [48], which have employed 3D-CNN-based classification. Above 90% accuracy is shown in [78] and [44]. The other studies perceived the accuracy ranges from 70%-80%.…”

Section: Performance Analysismentioning

confidence: 80%

“…In short, according to the proposed work, 2D-CNN improved the accuracy of classification by reducing training parameters compared to 3D CNN. A large portion of the reported approaches have applied DNN-based classification technique to diagnose SZ [35,55,78,79]. Srinivasagopalan et al claim that DL can be a paradigm shift for SZ diagnosis [78].…”

Section: Schizophreniamentioning

confidence: 99%

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Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson’s disease and schizophrenia

et al. 2020

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Neuroimaging, in particular magnetic resonance imaging (MRI), has been playing an important role in understanding brain functionalities and its disorders during the last couple of decades. These cutting-edge MRI scans, supported by high-performance computational tools and novel ML techniques, have opened up possibilities to unprecedentedly identify neurological disorders. However, similarities in disease phenotypes make it very difficult to detect such disorders accurately from the acquired neuroimaging data. This article critically examines and compares performances of the existing deep learning (DL)-based methods to detect neurological disorders—focusing on Alzheimer’s disease, Parkinson’s disease and schizophrenia—from MRI data acquired using different modalities including functional and structural MRI. The comparative performance analysis of various DL architectures across different disorders and imaging modalities suggests that the Convolutional Neural Network outperforms other methods in detecting neurological disorders. Towards the end, a number of current research challenges are indicated and some possible future research directions are provided.

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“…Recently, deep learning classifiers have been applied widely in studies involving the use of neuroimaging data for classification. So far, previous work has used the desired features directly as a single input to the learning framework without any subdomaining of the feature set (Srinivasagopalan et al, 2019;Ulloa et al, 2015;Han et al, 2017). The prevailing methodologies inherently employ two implicit assumptions which may not necessarily hold, namely a homogeneity in the feature set for use as a single input set and a high uniformity in the complexity of feature interactions between subdomains, ignoring the need for flexible architectures.…”

Section: Introductionmentioning

confidence: 99%

Building Models of Functional Interactions Among Brain Domains that Encode Varying Information Complexity: A Schizophrenia Case Study

Batta

Abrol

et al. 2020

Preprint

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Revealing associations among various structural and functional patterns of the brain can yield highly informative results about the healthy and disordered brain. Studies using neuroimaging data have more recently begun to utilize the information within as well as across various functional and anatomical domains (i.e., groups of brain networks). However, most whole-brain approaches assume similar complexity of interactions throughout the brain. Here we investigate the hypothesis that interactions between brain networks capture varying amounts of complexity, and that we can better capture this information by varying the complexity of the model subspace structure based on available training data. To do this, we employ a Bayesian optimization-based framework known as the Tree Parzen Estimator (TPE) to identify, exploit and analyze patterns of variation in the information encoded by temporal information extracted from functional magnetic resonance imaging (fMRI) subdomains of the brain. Using a repeated cross-validation procedure on a schizophrenia classification task, we demonstrate evidence that interactions between specific functional subdomains are better characterized by more sophisticated model architectures compared to less complicated ones required by the others for optimally contributing towards classification and understanding the brain’s functional interactions. We show that functional subdomains known to be involved in schizophrenia require more complex architectures to optimally unravel discriminatory information about the disorder. Our study points to the need for adaptive, hierarchical learning frameworks that cater differently to the features from different subdomains, not only for a better prediction but also for enabling the identification of features predicting the outcome of interest.

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A deep learning approach for diagnosing schizophrenic patients

Cited by 43 publications

References 27 publications

A Survey on Deep Learning for Neuroimaging-Based Brain Disorder Analysis

A Survey on Deep Learning for Neuroimaging-Based Brain Disorder Analysis

Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson’s disease and schizophrenia

Building Models of Functional Interactions Among Brain Domains that Encode Varying Information Complexity: A Schizophrenia Case Study

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