Transfer Learning Assisted Classification and Detection of Alzheimer’s Disease Stages Using 3D MRI Scans

Maqsood, Muazzam; Nazir, Faria; Khan, Umair; Aadil, Farhan; Jamal, Habibullah; Mehmood, Irfan; Song, Ohyoung

doi:10.3390/s19112645

Cited by 154 publications

(69 citation statements)

References 34 publications

(49 reference statements)

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“…To stop the overfitting, we used early stopping. The classification results obtained by the proposed model were evaluated using the different evaluation metrics [6], and we obtained 99.05% test accuracy. We used the Monte Carlo method to check the significance of the classification results under optimal parameters.…”

Section: Resultsmentioning

confidence: 99%

A Deep Siamese Convolution Neural Network for Multi-Class Classification of Alzheimer Disease

et al. 2020

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Alzheimer’s disease (AD) may cause damage to the memory cells permanently, which results in the form of dementia. The diagnosis of Alzheimer’s disease at an early stage is a problematic task for researchers. For this, machine learning and deep convolutional neural network (CNN) based approaches are readily available to solve various problems related to brain image data analysis. In clinical research, magnetic resonance imaging (MRI) is used to diagnose AD. For accurate classification of dementia stages, we need highly discriminative features obtained from MRI images. Recently advanced deep CNN-based models successfully proved their accuracy. However, due to a smaller number of image samples available in the datasets, there exist problems of over-fitting hindering the performance of deep learning approaches. In this research, we developed a Siamese convolutional neural network (SCNN) model inspired by VGG-16 (also called Oxford Net) to classify dementia stages. In our approach, we extend the insufficient and imbalanced data by using augmentation approaches. Experiments are performed on a publicly available dataset open access series of imaging studies (OASIS), by using the proposed approach, an excellent test accuracy of 99.05% is achieved for the classification of dementia stages. We compared our model with the state-of-the-art models and discovered that the proposed model outperformed the state-of-the-art models in terms of performance, efficiency, and accuracy.

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Section: Resultsmentioning

confidence: 99%

A Deep Siamese Convolution Neural Network for Multi-Class Classification of Alzheimer Disease

et al. 2020

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“…Because the number of subjects used in our study was 10 times greater than this last study, it is not fair to compare this study with our proposed method. [18] achieved 89.66% accuracy in binary AD classification (recognizing AD from subjects) but they achieved 92.85% accuracy in multiple classification using transfer learning. Using only MRI data [24] could classify AD vs. NC with an accuracy rate of 93.01%.…”

Section: Comparison With the State-of-the-art Methodsmentioning

confidence: 99%

“…These brain-observing techniques using machine learning can provide tools to overcome brain dysfunction problems. These combined techniques can use different modalities including MRI, PET, and other neurological data to diagnose AD/MCI patients from healthy people [18][19][20][21]. In [22] 50 MRI images from the OASIS dataset were used for characterization of MRIs of brains affected with Alzheimer's disease by fractal descriptors.…”

Section: Introductionmentioning

confidence: 99%

“…They reported complete performance (100% accuracy) in distinguishing between the two groups. [18] reported a multiple classification using transfer learning on AD, while [24] classified AD vs. NC with a great rate of accuracy using only MRI data. [25] classified progressive MCI vs. static MCI using combined MRI, APOe4 genetic, and cognitive measures include and APOe4 genotyping.…”

Section: Introductionmentioning

confidence: 99%

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Emphasis Learning, Features Repetition in Width Instead of Length to Improve Classification Performance: Case Study—Alzheimer’s Disease Diagnosis

Akramifard

Balafar

Razavi

et al. 2020

Sensors

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In the past decade, many studies have been conducted to advance computer-aided systems for Alzheimer's disease (AD) diagnosis. Most of them have recently developed systems concentrated on extracting and combining features from MRI, PET, and CSF. For the most part, they have obtained very high performance. However, improving the performance of a classification problem is complicated, specifically when the model's accuracy or other performance measurements are higher than 90%. In this study, a novel methodology is proposed to address this problem, specifically in Alzheimer's disease diagnosis classification. This methodology is the first of its kind in the literature, based on the notion of replication on the feature space instead of the traditional sample space. Briefly, the main steps of the proposed method include extracting, embedding, and exploring the best subset of features. For feature extraction, we adopt VBM-SPM; for embedding features, a concatenation strategy is used on the features to ultimately create one feature vector for each subject. Principal component analysis is applied to extract new features, forming a low-dimensional compact space. A novel process is applied by replicating selected components, assessing the classification model, and repeating the replication until performance divergence or convergence. The proposed method aims to explore most significant features and highest-preforming model at the same time, to classify normal subjects from AD and mild cognitive impairment (MCI) patients. In each epoch, a small subset of candidate features is assessed by support vector machine (SVM) classifier. This repeating procedure is continued until the highest performance is achieved. Experimental results reveal the highest performance reported in the literature for this specific classification problem. We obtained a model with accuracies of 98.81%, 81.61%, and 81.40% for AD vs. normal control (NC), MCI vs. NC, and AD vs. MCI classification, respectively.

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“…These brain observing techniques, using machine learning can provide nice tools to diagnosis and overcome brain's dysfunction problems. These combined techniques can use different modalities including MRI, PET, and other neurological data to diagnose AD/Mild Cognitive Impairment (MCI) patients from healthy people (NC) (Garyfallou et al, 2017;Islam et al, 2018;Maqsood et al, 2019;Toro & Gonzalo Martin, 2018). We can be sure that there exist unsight features among analyzed data in this area, that can help us for diagnosis and prognosis of AD/MCI.…”

Section: Introductionmentioning

confidence: 99%

Emphasis Learning (EL), Feature Repetition in Width Instead of Length, to Obtain Better Classification Performance; Case Study: Alzheimer’s Disease Diagnosis

Akramifard¹,

Balafar²,

Razavi³

et al. 2019

Preprint

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A method for classification is introduced in this article, and it is tested on ADNI database to diagnose alzheimer’s disease (AD). It is obvious that tunning the performance of a classification to get better results is a complicated problem, and when we want model’s accuracy or other peformance measurments higher than 90%, the problem will be more complicated. In this study, we tried and succeeded to discover a method to solve this problem. The final feature set can be used clustering too, because outgrowth feature set of the proposed method is invigorated. In the recent years, a lot of activities is done to develop computer aided systems (CAD) for alzheimer’s disease diagnosis. Most of these recently developed systems concenterated on extracting and combining features from MRI, PET, CSF, and …; in this article, we made attempt to do so and utilized one more technique to increase classification performance. Finding and producing the best features to solve three binary classiﬁcation problems of AD vs. Normal Control (NC), Mild Cognitive Impairment (MCI) vs. NC, and MCI vs. AD are the purposes of this article. Experiments indicate performance and effectiveness rates of the proposed method, which are accuracies of 98.81%, 81.61%, and 81.40% for AD vs. NC, MCI vs. NC, and AD vs. MCI classification problems, respectively. As can be seen, using this method increased the performance of the three binary problems incredibly.

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Transfer Learning Assisted Classification and Detection of Alzheimer’s Disease Stages Using 3D MRI Scans

Cited by 154 publications

References 34 publications

A Deep Siamese Convolution Neural Network for Multi-Class Classification of Alzheimer Disease

A Deep Siamese Convolution Neural Network for Multi-Class Classification of Alzheimer Disease

Emphasis Learning, Features Repetition in Width Instead of Length to Improve Classification Performance: Case Study—Alzheimer’s Disease Diagnosis

Emphasis Learning (EL), Feature Repetition in Width Instead of Length, to Obtain Better Classification Performance; Case Study: Alzheimer’s Disease Diagnosis

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Transfer Learning Assisted Classification and Detection of Alzheimer’s Disease Stages Using 3D MRI Scans

Cited by 154 publications

References 34 publications

A Deep Siamese Convolution Neural Network for Multi-Class Classification of Alzheimer Disease

A Deep Siamese Convolution Neural Network for Multi-Class Classification of Alzheimer Disease

Emphasis Learning, Features Repetition in Width Instead of Length to Improve Classification Performance: Case Study—Alzheimer’s Disease Diagnosis

Emphasis Learning (EL), Feature Repetition in Width Instead of Length, to Obtain Better Classification Performance; Case Study: Alzheimer&rsquo;s Disease Diagnosis

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Emphasis Learning (EL), Feature Repetition in Width Instead of Length, to Obtain Better Classification Performance; Case Study: Alzheimer’s Disease Diagnosis