Deep Residual Nets for Improved Alzheimer's Diagnosis

Valliani, Aly; Soni, Ameet

doi:10.1145/3107411.3108224

Cited by 57 publications

(35 citation statements)

References 3 publications

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“…The authors of [62,63,64] use 3D convolutions. The authors of [31,32,33,65] use the whole brain scans. Our method remains "light" in the sense that we focus only on one ROI, which is the biomarker of AD, the Hippocampal ROI.…”

Section: Discussion and Comparison With Literature Reviewmentioning

confidence: 99%

“…They have achieved an overall accuracy of 98.33% for AD/NC classification. In [33] , the authors used two networks, a baseline single-layer CNN and a pretrained ResNet network, they used a single 2D axial slice per subject (median slice from the 3D volume) as an input, the baseline CNN network was composed of only one convolutional layer and two FC layers. They studied the impact of transfer learning from RestNet trained on ImageNet, and the data augmentation in a real time, at training phase, they conclude that the ResNet architecture successfully fits to the MRI domain, and pretraining with data augmentation improves the prediction.…”

Section: Related Workmentioning

confidence: 99%

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Improving Alzheimer's stage categorization with Convolutional Neural Network using transfer learning and different magnetic resonance imaging modalities

Aderghal

Afdel

Benois-Pineau

et al. 2020

Heliyon

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Background Alzheimer's Disease (AD) is a neurodegenerative disease characterized by progressive loss of memory and general decline in cognitive functions. Multi-modal imaging such as structural MRI and DTI provide useful information for the classification of patients on the basis of brain biomarkers. Recently, CNN methods have emerged as powerful tools to improve classification using images. New Method In this paper, we propose a transfer learning scheme using Convolutional Neural Networks (CNNs) to automatically classify brain scans focusing only on a small ROI: e.g. a few slices of the hippocampal region. The network's architecture is similar to a LeNet-like CNN upon which models are built and fused for AD stage classification diagnosis. We evaluated various types of transfer learning through the following mechanisms: (i) cross-modal (sMRI and DTI) and (ii) cross-domain transfer learning (using MNIST) (iii) a hybrid transfer learning of both types. Results Our method shows good performances even on small datasets and with a limited number of slices of small brain region. It increases accuracy with more than 5 points for the most difficult classification tasks, i.e., AD/MCI and MCI/NC. Comparison with Existing Method(s) Our methodology provides good accuracy scores for classification over a shallow convolutional network. Besides, we focused only on a small region; i.e., the hippocampal region, where few slices are selected to feed the network. Also, we used cross-modal transfer learning. Conclusions Our proposed method is suitable for working with a shallow CNN network for low-resolution MRI and DTI scans. It yields to significant results even if the model is trained on small datasets, which is often the case in medical image analysis.

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Section: Discussion and Comparison With Literature Reviewmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Improving Alzheimer's stage categorization with Convolutional Neural Network using transfer learning and different magnetic resonance imaging modalities

Aderghal

Afdel

Benois-Pineau

et al. 2020

Heliyon

View full text Add to dashboard Cite

show abstract

“…Transfer learning is also more comfortable with small projects and produces higher performance than planning from the beginning [ 53 ]. Payan and Montana proposed classifying AD stages, namely, MCI, AD, and standard control [ 54 ] (NC).…”

Section: Research Backgroundmentioning

confidence: 99%

Diagnosis of Alzheimer’s Disease Severity with fMRI Images Using Robust Multitask Feature Extraction Method and Convolutional Neural Network (CNN)

Amini

Pedram

Moradi

et al. 2021

Computational and Mathematical Methods in Medicine

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The automatic diagnosis of Alzheimer’s disease plays an important role in human health, especially in its early stage. Because it is a neurodegenerative condition, Alzheimer’s disease seems to have a long incubation period. Therefore, it is essential to analyze Alzheimer’s symptoms at different stages. In this paper, the classification is done with several methods of machine learning consisting of K -nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), linear discrimination analysis (LDA), and random forest (RF). Moreover, novel convolutional neural network (CNN) architecture is presented to diagnose Alzheimer’s severity. The relationship between Alzheimer’s patients’ functional magnetic resonance imaging (fMRI) images and their scores on the MMSE is investigated to achieve the aim. The feature extraction is performed based on the robust multitask feature learning algorithm. The severity is also calculated based on the Mini-Mental State Examination score, including low, mild, moderate, and severe categories. Results show that the accuracy of the KNN, SVM, DT, LDA, RF, and presented CNN method is 77.5%, 85.8%, 91.7%, 79.5%, 85.1%, and 96.7%, respectively. Moreover, for the presented CNN architecture, the sensitivity of low, mild, moderate, and severe status of Alzheimer patients is 98.1%, 95.2%,89.0%, and 87.5%, respectively. Based on the findings, the presented CNN architecture classifier outperforms other methods and can diagnose the severity and stages of Alzheimer’s disease with maximum accuracy.

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“…Other approaches pre-trained a stacked AE using natural images (everyday images) prior to training on brain MR images in order to learn more high-fidelity anatomical features, such as gray matter and structural deformities, for incorporation into a CNN [35]. Variations on these approaches have been used to incrementally improve diagnostic performance [36][37][38][39][40][41][42].…”

Section: Medical Image Classificationmentioning

confidence: 99%

Deep Learning and Neurology: A Systematic Review

2019

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Deciphering the massive volume of complex electronic data that has been compiled by hospital systems over the past decades has the potential to revolutionize modern medicine, as well as present significant challenges. Deep learning is uniquely suited to address these challenges, and recent advances in techniques and hardware have poised the field of medical machine learning for transformational growth. The clinical neurosciences are particularly well positioned to benefit from these advances given the subtle presentation of symptoms typical of neurologic disease. Here we review the various domains in which deep learning algorithms have already provided impetus for change-areas such as medical image analysis for the improved diagnosis of Alzheimer's disease and the early detection of acute neurologic events; medical image segmentation for quantitative evaluation of neuroanatomy and vasculature; connectome mapping for the diagnosis of Alzheimer's, autism spectrum disorder, and attention deficit hyperactivity disorder; and mining of microscopic electroencephalogram signals and granular genetic signatures. We additionally note important challenges in the integration of deep learning tools in the clinical setting and discuss the barriers to tackling the challenges that currently exist.

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Deep Residual Nets for Improved Alzheimer's Diagnosis

Cited by 57 publications

References 3 publications

Improving Alzheimer's stage categorization with Convolutional Neural Network using transfer learning and different magnetic resonance imaging modalities

Improving Alzheimer's stage categorization with Convolutional Neural Network using transfer learning and different magnetic resonance imaging modalities

Diagnosis of Alzheimer’s Disease Severity with fMRI Images Using Robust Multitask Feature Extraction Method and Convolutional Neural Network (CNN)

Deep Learning and Neurology: A Systematic Review

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