A Deep Learning Approach for Automated Diagnosis and Multi-Class Classification of Alzheimer’s Disease Stages Using Resting-State fMRI and Residual Neural Networks

Ramzan, Farheen; Khan, Muhammad Usman Ghani; Rehmat, Asim; Iqbal, Sajid; Saba, Tanzila; Mehmood, Zahid

doi:10.1007/s10916-019-1475-2

Cited by 267 publications

(189 citation statements)

References 45 publications

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“…It also used a pre-trained model and obtained 95.23% classification accuracy. In 2020, Ramzan et al published “ A Deep Learning Approach for Automated Diagnosis and Multi-Class Classification of Alzheimer’s Disease Stages Using Resting-State fMRI and Residual Neural Networks ” [ 35 ]. It divided the disease into six categories: cognitively normal, significant memory concern, early mild cognitive impairment, mild cognitive impairment, late mild cognitive impairment, and Alzheimer’s disease.…”

Section: Discussionmentioning

confidence: 99%

Classification of the Multiple Stages of Parkinson’s Disease by a Deep Convolution Neural Network Based on 99mTc-TRODAT-1 SPECT Images

et al. 2020

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Single photon emission computed tomography (SPECT) has been employed to detect Parkinson’s disease (PD). However, analysis of the SPECT PD images was mostly based on the region of interest (ROI) approach. Due to limited size of the ROI, especially in the multi-stage classification of PD, this study utilizes deep learning methods to establish a multiple stages classification model of PD. In the retrospective study, the 99mTc-TRODAT-1 was used for brain SPECT imaging. A total of 202 cases were collected, and five slices were selected for analysis from each subject. The total number of images was thus 1010. According to the Hoehn and Yahr Scale standards, all the cases were divided into healthy, early, middle, late four stages, and HYS I~V six stages. Deep learning is compared with five convolutional neural networks (CNNs). The input images included grayscale and pseudo color of two types. The training and validation sets were 70% and 30%. The accuracy, recall, precision, F-score, and Kappa values were used to evaluate the models’ performance. The best accuracy of the models based on grayscale and color images in four and six stages were 0.83 (AlexNet), 0.85 (VGG), 0.78 (DenseNet) and 0.78 (DenseNet).

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Section: Discussionmentioning

confidence: 99%

Classification of the Multiple Stages of Parkinson’s Disease by a Deep Convolution Neural Network Based on 99mTc-TRODAT-1 SPECT Images

et al. 2020

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“…Another strategy is model shifting, where the anticipated location of the hemodynamic response function (HRF) is differing [62]. Slice timing correction can also be executed by using FEAT module of FSL library [47]. Moreover, Least squares approach with 6 parameter spatial transformation is also used as a method for slice timing correction in [51].…”

Section: Correctionmentioning

confidence: 99%

“…Motion correction minimizes the impact of movement on image data by orienting the data to a reference time volume, application of which is found in [48,54,63]. Motion correction can also be performed by MCFLIRT module of FSL library [31,47].…”

Section: Correctionmentioning

confidence: 99%

“…A common technique is affine linear transformation (ALT) [31,37]. The FMRIB's Linear Image Registration Tool or FLIRT module is also used for this purpose [47]. Moreover, diffeomorphic anatomical registration through exponentiated Lie algebra (DARTEL) procedure is used for spatial normalization in [50].…”

Section: Normalization (Nm)mentioning

confidence: 99%

“…Within this context, the objective of spatial smoothing copes with functional anatomical variability which has not been compensated by spatial normalization ("warping") thus, improving the SNR. Spatial smoothing is conducted with a spatially stationary Gaussian filter in which the user must ordain the kernel width in mm "full width half max" [31,47,48,[52][53][54]. This Gaussian kernel is a kernel which possesses the shape of a normal distribution curve [66].…”

Section: Smoothing (Sm)mentioning

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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Label‐Free Imaging Flow Cytometry for Cell Classification Based on Multiple Interferometric Projections Using Deep Learning

Cohen,

Dudaie,

Barnea

et al. 2023

Advanced Intelligent Systems

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A new label‐free imaging flow cytometry method for noninvasive and automated biological cell classification is presented. Each cell is rolled during flow, and its off‐axis holograms from multiple viewpoints are acquired. Using the reconstructed quantitative phase profiles of the cell projections, highly discriminating features, enabling cell detection, classification, and differentiation, are extracted via a modified ResNet‐18 deep convolutional neural network architecture. The model is first validated by classifying metastatic breast carcinoma cells (MCF‐7) and normal human mammary epithelial cells (MCF‐10A). An increase in classification accuracy by 1% is achieved when processing five interferometric projections versus processing a single interferometric projection. This model is further tested on four types of white blood cells and exhibits an accuracy increase of 5% when processing 12 interferometric projections versus processing a single interferometric projection. This approach is shown to be superior to that of using conventional 2D‐rotation augmentation, and can be used to decrease substantially the number of cell examples needed for training the classification model without impairing the results. This novel concept has great potential to be incorporated into label‐free imaging flow cytometry and improve cell classification, and be used to detect various types of medical conditions and diseases.

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A Deep Learning Approach for Automated Diagnosis and Multi-Class Classification of Alzheimer’s Disease Stages Using Resting-State fMRI and Residual Neural Networks

Cited by 267 publications

References 45 publications

Classification of the Multiple Stages of Parkinson’s Disease by a Deep Convolution Neural Network Based on 99mTc-TRODAT-1 SPECT Images

Classification of the Multiple Stages of Parkinson’s Disease by a Deep Convolution Neural Network Based on 99mTc-TRODAT-1 SPECT Images

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

Label‐Free Imaging Flow Cytometry for Cell Classification Based on Multiple Interferometric Projections Using Deep Learning

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