Applying deep learning models to structural MRI for stage prediction of Alzheimer’s disease

Yiğit, Altuğ; Işık, Zerrin

doi:10.3906/elk-1904-172

Cited by 51 publications

(23 citation statements)

References 25 publications

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“…It refers to the process of modifying or enhancing an image by emphasizing certain features or removing other features [50,51]. Growing skull fracture or GSF has been reported in this study for reducing sharp pixel transitions between pixels [46,58].…”

Section: Filtering (F)mentioning

confidence: 84%

“…Contrast enhancement (CE) A contrast enhancement method can be used to stop the clustering in histogram with the purpose of correcting the distribution. In [46], a CE method named CLAHE was used.…”

Section: Scalingmentioning

confidence: 99%

“…Data augmentation (DA) Data augmentation techniques are applied when the number of images for different classes become unbalanced [46,57].…”

Section: Linear Regression (Lr)mentioning

confidence: 99%

“…Consecutively another CNN-based model has been proposed in recent times in [46]. T1-weighted volumetric MR images have been used to diagnose AD and MCI.…”

Section: Alzheimer's Diseasementioning

confidence: 99%

“…There is a total of 708 scans in this database which had been collected at intervals from 2 weeks to 2 years conducted by the same radiographer using the same scanner. It also accompanied information on gender, age and MMSE scores [46,84].…”

Section: Miriadmentioning

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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Section: Filtering (F)mentioning

confidence: 84%

Section: Scalingmentioning

confidence: 99%

“…Data augmentation (DA) Data augmentation techniques are applied when the number of images for different classes become unbalanced [46,57].…”

Section: Linear Regression (Lr)mentioning

confidence: 99%

“…Consecutively another CNN-based model has been proposed in recent times in [46]. T1-weighted volumetric MR images have been used to diagnose AD and MCI.…”

Section: Alzheimer's Diseasementioning

confidence: 99%

Section: Miriadmentioning

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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Predictive modelling of brain disorders with magnetic resonance imaging: A systematic review of modelling practices, transparency, and interpretability in the use of convolutional neural networks

O'Connell,

Cannon,

Broin

2023

Human Brain Mapping

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Brain disorders comprise several psychiatric and neurological disorders which can be characterized by impaired cognition, mood alteration, psychosis, depressive episodes, and neurodegeneration. Clinical diagnoses primarily rely on a combination of life history information and questionnaires, with a distinct lack of discriminative biomarkers in use for psychiatric disorders. Symptoms across brain conditions are associated with functional alterations of cognitive and emotional processes, which can correlate with anatomical variation; structural magnetic resonance imaging (MRI) data of the brain are therefore an important focus of research, particularly for predictive modelling. With the advent of large MRI data consortia (such as the Alzheimer's Disease Neuroimaging Initiative) facilitating a greater number of MRI‐based classification studies, convolutional neural networks (CNNs)—deep learning models well suited to image processing tasks—have become increasingly popular for research into brain conditions. This has resulted in a myriad of studies reporting impressive predictive performances, demonstrating the potential clinical value of deep learning systems. However, methodologies can vary widely across studies, making them difficult to compare and/or reproduce, potentially limiting their clinical application. Here, we conduct a qualitative systematic literature review of 55 studies carrying out CNN‐based predictive modelling of brain disorders using MRI data and evaluate them based on three principles—modelling practices, transparency, and interpretability. We propose several recommendations to enhance the potential for the integration of CNNs into clinical care.

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Structural biomarker‐based Alzheimer's disease detection via ensemble learning techniques

Shukla,

Tiwari,

Tiwari

2023

Int J Imaging Syst Tech

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Alzheimer's disease (AD) is a degenerative neurological disorder with incurable characteristics. To identify the substantial solution, we used a structural biomarker (structural magnetic resonance imaging) to see the neurostructural changes in the different regions of the brain of AD, mild cognitive impairment, and cognitive normal subjects. In this study, we detected the AD and their subtypes by using the traditional machine learning and ensemble learning models. It is also identified the relative impact score of various cortical and subcortical regions of AD and their subtypes. Experimental study contains two levels of classification: binary and multiclass. The Ensemble_LR_SVM model in binary classification has 99% accuracy in detection. Random forest model in the multiclass has 82% of accuracy. In the cortical‐subcortical analysis, the right hemisphere's parahippocampal and entorhinal regions were discovered to be the most influential. Similarly, the inferior temporal and isthmus cingulate regions had a significant influence in the left hemisphere.

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Applying deep learning models to structural MRI for stage prediction of Alzheimer’s disease

Cited by 51 publications

References 25 publications

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

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

Predictive modelling of brain disorders with magnetic resonance imaging: A systematic review of modelling practices, transparency, and interpretability in the use of convolutional neural networks

Structural biomarker‐based Alzheimer's disease detection via ensemble learning techniques

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