Deep Learning: Classification and Automated Detection Earlier of Alzheimer’s Disease Using Brain MRI Images

Neuroimaging data analysis has attracted a great deal of attention with respect to the accurate diagnosis of Alzheimer’s disease (AD). Magnetic Resonance Imaging (MRI) scanners have thus been commonly used to study AD-related brain structural variations, providing images that demonstrate both morphometric and anatomical changes in the human brain. Deep learning algorithms have already been effectively exploited in other medical image processing applications to identify features and recognise patterns for many diseases that affect the brain and other organs; this paper extends on this to describe a novel computer aided software pipeline for the classification and early diagnosis of AD. The proposed method uses two types of three-dimensional Convolutional Neural Networks (3D CNN) to facilitate brain MRI data analysis and automatic feature extraction and classification, so that pre-processing and post-processing are utilised to normalise the MRI data and facilitate pattern recognition. The experimental results show that the proposed approach achieves 97.5%, 82.5%, and 83.75% accuracy in terms of binary classification AD vs. cognitively normal (CN), CN vs. mild cognitive impairment (MCI) and MCI vs. AD, respectively, as well as 85% accuracy for multi class-classification, based on publicly available data sets from the Alzheimer’s disease Neuroimaging Initiative (ADNI).

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A Novel Deep Learning Based Multi-class Classification Method for Alzheimer’s Disease Detection Using Brain MRI Data

Brain Informatics - Lecture Notes in Computer Science ◽

10.1007/978-3-319-70772-3_20 ◽

2017 ◽

pp. 213-222 ◽

Cited By ~ 25

Author(s):

Jyoti Islam ◽

Yanqing Zhang

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Deep Learning ◽

Brain Mri ◽

Classification Method ◽

Disease Detection ◽

Multi Class Classification

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Convolutional Autoencoder based Deep Learning Approach for Alzheimer's Disease Diagnosis using Brain MRI

2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS) ◽

10.1109/cbms52027.2021.00097 ◽

2021 ◽

Author(s):

Ekin Yagis ◽

Alba G. Seco de Herrera ◽

Luca Citi

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Deep Learning ◽

Brain Mri ◽

Disease Diagnosis ◽

Learning Approach ◽

Convolutional Autoencoder ◽

Alzheimer’S Disease Diagnosis

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Classification of Sex and Alzheimer’s Disease via Brain Imaging-Based Deep Learning on 85,721 Samples

10.1101/2020.08.18.256594 ◽

2020 ◽

Author(s):

Bin Lu ◽

Hui-Xian Li ◽

Zhi-Kai Chang ◽

Le Li ◽

Ning-Xuan Chen ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Deep Learning ◽

Individual Differences ◽

Brain Imaging ◽

Transfer Learning ◽

Brain Mri ◽

Classification Model ◽

Parahippocampal Gyrus ◽

Industrial Grade

AbstractBeyond detecting brain damage or tumors, little success has been attained on identifying individual differences and brain disorders with magnetic resonance imaging (MRI). Here, we sought to build industrial-grade brain imaging-based classifiers to infer two types of such inter-individual differences: sex and Alzheimer’s disease (AD), using deep learning/transfer learning on big data. We pooled brain structural data from 217 sites/scanners to constitute the largest brain MRI sample to date (85,721 samples from 50,876 participants), and applied a state-of-the-art deep convolutional neural network, Inception-ResNet-V2, to build a sex classifier with high generalizability. In cross-dataset-validation, the sex classification model was able to classify the sex of any participant with brain structural imaging data from any scanner with 94.9% accuracy. We then applied transfer learning based on this model to objectively diagnose AD, achieving 88.4% accuracy in cross-site-validation on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset and 91.2% / 86.1% accuracy for a direct test on two unseen independent datasets (AIBL / OASIS). Directly testing this AD classifier on brain images of unseen mild cognitive impairment (MCI) patients, the model correctly predicted 63.2% who eventually converted into AD, versus predicting 22.1% as AD who did not convert into AD during follow-up. Predicted scores of the AD classifier correlated significantly with illness severity. By contrast, the transfer learning framework was unable to achieve practical accuracy for psychiatric disorders. To improve interpretability of the deep learning models, occlusion tests revealed that hypothalamus, superior vermis, thalamus, amygdala and limbic system areas were critical for predicting sex; hippocampus, parahippocampal gyrus, putamen and insula played key roles in predicting AD. Our trained model, code, preprocessed data and an online prediction website have been openly-shared to advance the clinical utility of brain imaging.

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Deep Learning Based Binary Classification for Alzheimer’s Disease Detection using Brain MRI Images

2020 15th IEEE Conference on Industrial Electronics and Applications (ICIEA) ◽

10.1109/iciea48937.2020.9248213 ◽

2020 ◽

Author(s):

Emtiaz Hussain ◽

Mahmudul Hasan ◽

Syed Zafrul Hassan ◽

Tanzina Hassan Azmi ◽

Md Anisur Rahman ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Deep Learning ◽

Binary Classification ◽

Brain Mri ◽

Disease Detection

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MRI Segmentation and Classification of Human Brain Using Deep Learning for Diagnosis of Alzheimer’s Disease: A Survey

Sensors ◽

10.3390/s20113243 ◽

2020 ◽

Vol 20 (11) ◽

pp. 3243 ◽

Cited By ~ 2

Author(s):

Nagaraj Yamanakkanavar ◽

Jae Young Choi ◽

Bumshik Lee

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Deep Learning ◽

Brain Structure ◽

State Of The Art ◽

Brain Mri ◽

Mri Segmentation ◽

Learning Methods ◽

The Brain

Many neurological diseases and delineating pathological regions have been analyzed, and the anatomical structure of the brain researched with the aid of magnetic resonance imaging (MRI). It is important to identify patients with Alzheimer’s disease (AD) early so that preventative measures can be taken. A detailed analysis of the tissue structures from segmented MRI leads to a more accurate classification of specific brain disorders. Several segmentation methods to diagnose AD have been proposed with varying complexity. Segmentation of the brain structure and classification of AD using deep learning approaches has gained attention as it can provide effective results over a large set of data. Hence, deep learning methods are now preferred over state-of-the-art machine learning methods. We aim to provide an outline of current deep learning-based segmentation approaches for the quantitative analysis of brain MRI for the diagnosis of AD. Here, we report how convolutional neural network architectures are used to analyze the anatomical brain structure and diagnose AD, discuss how brain MRI segmentation improves AD classification, describe the state-of-the-art approaches, and summarize their results using publicly available datasets. Finally, we provide insight into current issues and discuss possible future research directions in building a computer-aided diagnostic system for AD.

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An Alzheimer’s Disease Identification and Classification Model Based on the Convolutional Neural Network with Attention Mechanisms

Traitement du signal ◽

10.18280/ts.380533 ◽

2021 ◽

Vol 38 (5) ◽

pp. 1557-1564

Author(s):

Yin Chen

Keyword(s):

Neural Network ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Deep Learning ◽

Convolutional Neural Network ◽

Brain Mri ◽

Brain Regions ◽

Classification Model ◽

Model Based ◽

Disease Identification

MRI image analysis of brain regions based on deep learning can effectively reduce the workload of doctors in reading films and improve the accuracy of diagnosis. Therefore, deep learning models have great application prospects in the classification and prediction of Alzheimer’s patients and normal people. However, the existing research has ignored the correlation between small abnormalities in local brain regions and changes in brain tissues. To this end, this paper studies an Alzheimer’s disease identification and classification model based on the convolutional neural network (CNN) with attention mechanisms. In this paper, the attention mechanisms were introduced from the regional level and the feature level, and the information of brain MRI images was fused from multiple levels to find out the correlation between the slices in brain MRI images. Then, a spatio-temporal graph CNN with dual attention mechanisms was constructed, which made the network model more attentive to the salient channel features while eliminating the impact of certain noise features. The experimental results verified the effectiveness of the constructed model in identification and classification of Alzheimer’s disease.

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A Practical Alzheimer Disease Classifier via Brain Imaging-Based Deep Learning on 85,721 Samples

10.21203/rs.3.rs-1156067/v1 ◽

2021 ◽

Author(s):

Bin Lu ◽

Hui-Xian Li ◽

Zhi-Kai Chang ◽

Le Li ◽

Ning-Xuan Chen ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Deep Learning ◽

Transfer Learning ◽

Brain Mri ◽

Machine Learning Algorithms ◽

Training Data ◽

Generalization Capability ◽

Brain Images ◽

Magnetic Resonance Imaging Mri

Abstract BackgroundBeyond detecting brain lesions or tumors, comparatively little success has been attained in identifying brain disorders such as Alzheimer’s disease (AD), based on magnetic resonance imaging (MRI). Many machine learning algorithms to detect AD have been trained using limited training data, meaning they often generalize poorly when applied to scans from previously unseen populations. Therefore, we built a practical brain MRI-based AD diagnostic classifier using deep learning/transfer learning on dataset of unprecedented size and diversity. MethodsA retrospective MRI dataset pooled from more than 217 sites/scanners constituted the largest brain MRI sample to date (85,721 scans from 50,876 participants) between January 2017 and August 2021. Next, a state-of-the-art deep convolutional neural network, Inception-ResNet-V2, was built as a sex classifier with high generalization capability. The sex classifier achieved 94.9% accuracy and served as a base model in transfer learning for the objective diagnosis of AD. FindingsAfter transfer learning, the model fine-tuned for AD classification achieved 91.3% accuracy in leave-sites-out cross-validation on the Alzheimer's Disease Neuroimaging Initiative (ADNI, 6,857 samples) dataset and 94.2%/93.6%/90.5% accuracy for direct tests on three unseen independent datasets (AIBL, 669 samples / MIRIAD, 644 samples / OASIS, 1,123 samples). When this AD classifier was tested on brain images from unseen mild cognitive impairment (MCI) patients, MCI patients who finally converted to AD were 3 times more likely to be predicted as AD than MCI patients who did not convert (65.2% vs 20.6%). Predicted scores from the AD classifier showed significant correlations with illness severity. InterpretationIn sum, the proposed AD classifier could offer a medical-grade marker that have potential to be integrated into AD diagnostic practice.

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