scholarly journals Classification of Alzheimer’s Disease Patients Using Texture Analysis and Machine Learning

2021 ◽  
Vol 4 (3) ◽  
pp. 49
Author(s):  
Sumit Salunkhe ◽  
Mrinal Bachute ◽  
Shilpa Gite ◽  
Nishad Vyas ◽  
Saanil Khanna ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Complex Disease ◽  
Texture Classification ◽  
Region Of Interest ◽  
Structural Data ◽  
Support Vector ◽  
Magnetic Resonance Imaging Mri ◽  
The Many ◽  
Occurrence Matrix

Alzheimer’s disease (AD) has been studied extensively to understand the nature of this complex disease and address the many research gaps concerning prognosis and diagnosis. Several studies based on structural and textural characteristics have already been conducted to aid in identifying AD patients. In this work, an image processing methodology was used to extract textural information and classify the patients into two groups: AD and Cognitively Normal (CN). The Gray Level Co-occurrence Matrix (GLCM) was employed since it is a strong foundation for texture classification. Various textural parameters derived from the GLCM aided in deciphering the characteristics of a Magnetic Resonance Imaging (MRI) region of interest (ROI). Several commonly used image classification algorithms were employed. MATLAB was used to successfully derive 20 features based on the GLCM of the MRI dataset. Based on the data analysis, 8 of the 20 features were determined as significant elements. Ensemble (90.2%), Decision Trees (88.5%), and Support Vector Machine (SVM) (87.2%) were the best performing classifiers. It was observed in GLCM that as the distance (d) between pixels increased, the classification accuracy decreased. The best result was observed for GLCM with d = 1 and direction (d, d, −d) with age and structural data.

scholarly journals Klasifikasi Alzheimer dan Non Alzheimer Menggunakan Fuzzy C-Mean, Gray Level Co-Occurence Matrix dan Support Vector Machine

2018 ◽  
Vol 4 (2) ◽  
pp. 83-89
Author(s):  
Dian C. Rini Novitasari
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Support Vector Machine ◽  
Cognitive Disorders ◽  
Support Vector ◽  
Gray Level ◽  
Accuracy Result ◽  
Magnetic Resonance Imaging Mri ◽  
Occurrence Matrix ◽  
Patients Experience

Based on the Alzheimer's Charter, 2-3 million cases of dementia by Alzheimer's disease occur every year. People with Alzheimer's disease experience memory and cognitive disorders progressively for 3 to 9 years. Patients experience confusion in understanding the question and have a chaotic sequence of memory, which can interfere with daily activities and unchecked well, it cause death. The classification system is based on Alzheimer's and non-Alzheimer's disease Magnetic Resonance Imaging (MRI) using Support Vector Machine (SVM). The feature data segmentation using Fuzzy C-Means (FCM) and feature extraction using Gray Level Co-Occurrence Matrix (GLCM) and give accuracy result of 93.33%.

scholarly journals Accelerating Hyperparameter Tuning in Machine Learning for Alzheimer’s Disease With High Performance Computing

2021 ◽  
Vol 4 ◽  
Author(s):  
Fan Zhang ◽  
Melissa Petersen ◽  
Leigh Johnson ◽  
James Hall ◽  
Sid E. O’Bryant
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
High Performance ◽  
Support Vector ◽  
Massive Datasets ◽  
Complexity Of Algorithms ◽  
Magnetic Resonance Imaging Mri ◽  
Research Challenge ◽  
Improved Accuracy

Driven by massive datasets that comprise biomarkers from both blood and magnetic resonance imaging (MRI), the need for advanced learning algorithms and accelerator architectures, such as GPUs and FPGAs has increased. Machine learning (ML) methods have delivered remarkable prediction for the early diagnosis of Alzheimer’s disease (AD). Although ML has improved accuracy of AD prediction, the requirement for the complexity of algorithms in ML increases, for example, hyperparameters tuning, which in turn, increases its computational complexity. Thus, accelerating high performance ML for AD is an important research challenge facing these fields. This work reports a multicore high performance support vector machine (SVM) hyperparameter tuning workflow with 100 times repeated 5-fold cross-validation for speeding up ML for AD. For demonstration and evaluation purposes, the high performance hyperparameter tuning model was applied to public MRI data for AD and included demographic factors such as age, sex and education. Results showed that computational efficiency increased by 96%, which helped to shed light on future diagnostic AD biomarker applications. The high performance hyperparameter tuning model can also be applied to other ML algorithms such as random forest, logistic regression, xgboost, etc.

scholarly journals Prediction of Alzheimer’s Disease Using Machine Learning Classifiers

2020 ◽  
Vol 9 (3) ◽  
pp. 116-120
Author(s):  
Mansour Rezaei ◽  
Ehsan Zereshki ◽  
Soodeh Shahsavari ◽  
Mohammad Gharib Salehi ◽  
Hamid Sharini
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Correct Diagnosis ◽  
Medical Science ◽  
Screening Tests ◽  
Support Vector ◽  
Svm Classifier ◽  
Rbf Kernel ◽  
Magnetic Resonance Imaging Mri ◽  
The Brain

Background: Alzheimer’s disease (AD) is the most common brain failure for which no cure has yet been found. The disease starts with a disturbance in the brain structure and then it manifests itself clinically. Therefore, by timely and correct diagnosis of changes in the structure of the brain, the occurrence of this disease or at least its progression can be prevented. Due to the fact that magnetic resonance imaging (MRI) can be used to obtain very useful information from the brain, and also because it is non-invasive, this method has been considered by researchers. Materials and Methods: The data were obtained from an MRI database (MIRIAD) of 69 subjects including 46 AD patients and 23 healthy controls (HC). Individuals were categorized based on two criteria including NINCDS-ADRAD and MMSE, as the gold standard. In this paper, we used the support vector machine (SVM) and Bayesian SVM classifiers. Results: Using the SVM classifier with Gaussian radial basis function (RBF) kernel, we distinguished AD and HC with an accuracy of 88.34%. The most important regions of interest (ROIs) in this study included right para hippocampal gyrus, left para hippocampal gyrus, right hippocampus, and left hippocampus. Conclusion: This study showed that the SVM model with Gaussian RBF kernel can distinguish AD from HC with high accuracy. These studies are of great importance in medical science. Based on the results of this study, MRI centers and neurologists can perform AD screening tests in people over the age of 50 years.

scholarly journals Identification of Alzheimer’s Disease on the Basis of a Voxel-Wise Approach

2019 ◽  
Vol 9 (15) ◽  
pp. 3063
Author(s):  
Iman Beheshti ◽  
Hadi Mahdipour Hossein-Abad ◽  
Hiroshi Matsuda ◽  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Spatial Resolution ◽  
Feature Selection Method ◽  
Neuronal Loss ◽  
Support Vector ◽  
Whole Brain ◽  
Mri Features ◽  
Magnetic Resonance Imaging Mri ◽  
The Impact

Robust prediction of Alzheimer’s disease (AD) helps in the early diagnosis of AD and may support the treatment of AD patients. In this study, for early detection of AD and prediction of mild cognitive impairment (MCI) conversion, we develop an automatic computer-aided diagnosis (CAD) framework based on a merit-based feature selection method through a whole-brain voxel-wise analysis using baseline magnetic resonance imaging (MRI) data. We also explore the impact of different MRI spatial resolution on the voxel-wise metric AD classification and MCI conversion prediction. We assessed the proposed CAD framework using the whole-brain voxel-wise MRI features of 507 J-ADNI participants (146 healthy controls [HCs], 102 individuals with stable MCI [sMCI], 112 with progressive MCI [pMCI], and 147 with AD) among four clinically relevant pairs of diagnostic groups at different imaging resolutions (i.e., 2, 4, 8, and 16 mm). Using a support vector machine classifier through a 10-fold cross-validation strategy at a spatial resolution of 2 mm, the proposed CAD framework yielded classification accuracies of 91.13%, 74.77%, 81.12%, and 81.78% in identifying AD/healthy control, sMCI/pMCI, sMCI/AD, and pMCI/HC, respectively. The experimental results show that a lower spatial resolution (i.e., 2 mm) may provide more robust information to trace the neuronal loss-related brain atrophy in AD.

scholarly journals A Comprehensive Machine-Learning Model Applied to Magnetic Resonance Imaging (MRI) to Predict Alzheimer’s Disease (AD) in Older Subjects

2020 ◽  
Vol 9 (7) ◽  
pp. 2146
Author(s):  
Gopi Battineni ◽  
Nalini Chintalapudi ◽  
Francesco Amenta ◽  
Enea Traini
Keyword(s):  
Magnetic Resonance Imaging ◽  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Feature Selection ◽  
Magnetic Resonance ◽  
Neurodegenerative Disorder ◽  
Support Vector ◽  
Resonance Imaging ◽  
Magnetic Resonance Imaging Mri

Increasing evidence suggests the utility of magnetic resonance imaging (MRI) as an important technique for the diagnosis of Alzheimer’s disease (AD) and for predicting the onset of this neurodegenerative disorder. In this study, we present a sophisticated machine learning (ML) model of great accuracy to diagnose the early stages of AD. A total of 373 MRI tests belonging to 150 subjects (age ≥ 60) were examined and analyzed in parallel with fourteen distinct features related to standard AD diagnosis. Four ML models, such as naive Bayes (NB), artificial neural networks (ANN), K-nearest neighbor (KNN), and support-vector machines (SVM), and the receiver operating characteristic (ROC) curve metric were used to validate the model performance. Each model evaluation was done in three independent experiments. In the first experiment, a manual feature selection was used for model training, and ANN generated the highest accuracy in terms of ROC (0.812). In the second experiment, automatic feature selection was conducted by wrapping methods, and the NB achieved the highest ROC of 0.942. The last experiment consisted of an ensemble or hybrid modeling developed to combine the four models. This approach resulted in an improved accuracy ROC of 0.991. We conclude that the involvement of ensemble modeling, coupled with selective features, can predict with better accuracy the development of AD at an early stage.

Incorporating Attention Mechanism in Enhancing Classification of Alzheimer’s Disease

2021 ◽  
Author(s):  
Nur Amirah Abd Hamid ◽  
Mohd Ibrahim Shapiai ◽  
Uzma Batool ◽  
Ranjit Singh Sarban Singh ◽  
Muhamad Kamal Mohammed Amin ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Transfer Learning ◽  
Binary Classification ◽  
Region Of Interest ◽  
Local Features ◽  
Attention Mechanism ◽  
Medical Evaluation ◽  
Magnetic Resonance Imaging Mri

Alzheimer’s disease (AD) is a progressive and irreversible neurodegenerative disease that requires attentive medical evaluation. Therefore, diagnosing of AD accurately is crucial to provide the patients with appropriate treatment to slow down the progression of AD as well to facilitate the treatment interventions. To date, deep learning by means of convolutional neural networks (CNNs) has been widely used in diagnosing of AD. There are several well-established CNNs architectures that have been used in the image classification domain for magnetic resonance imaging (MRI) images analysis such as LeNet-5, Inception-V4, VGG-16 and Residual Network. However, these existing deep learning-based methods have lack of ability to be spatial invariance to the input data, due to overlooking some salient local features of the region of interest (ROI) (i.e., hippocampal). In medical image analysis, local features of MRI images are hard to exploit due to the small pixel size of ROI. On the other hand, CNNs requires large dataset sample to perform well, but we have limited number of MRI images to train, thus, leading to overfitting. Therefore, we propose a novel deep learning-based model without pre-processing techniques by incorporating attention mechanism and global average pooling (GAP) layer to VGG-16 architecture to capture the salient features of the MRI image for subtle discriminating of AD and normal control (NC). Also, we utilize transfer learning to surpass the overfitting issue. Experiment is performed on data collected from Open Access Series of Imaging Studies (OASIS) database. The accuracy performance of binary classification (AD vs NC) using proposed method significantly outperforms the existing methods, 12-layered CNNs (trained from scratch) and Inception-V4 (transfer learning) by increasing 1.93% and 3.43% of the accuracy. In conclusion, Attention-GAP model capable of improving and achieving notable classification accuracy in diagnosing AD.

Hybrid Feature Extraction and Classification for Alzheimer’s Disease Detection

2020 ◽  
Vol 17 (12) ◽  
pp. 5577-5581
Author(s):  
P. Sharmila ◽  
C. Rekha ◽  
D. Muruga Radha Devi ◽  
K. P. Revathi ◽  
K. Sornalatha
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Feature Extraction ◽  
Support Vector ◽  
Accurate Analysis ◽  
Mode Decomposition ◽  
Extraction Algorithm ◽  
Hybrid Feature Extraction ◽  
Comparative Results ◽  
Occurrence Matrix

Alzheimer’s disease (AD) is a serious neurological brain disease. It terminates brain cells, causing loss of memory, mental function and the capability to continue their daily actions. AD is incurable, but early detection can greatly improve symptoms. Machine learning can greatly develop the accurate analysis of AD. In this paper, we have implemented the two different hybrid algorithms for feature extraction and classification. Hybrid feature extraction algorithm is based on Empirical mode decomposition (EMD) and Gray-Level Co-Occurrence Matrix (GLCM), which is named as EMDGLCM. For classification purpose Support vector machine (SVM) and Convolution neural network (CNN) which is named as SVM-CNN. The proposed hybrid algorithm feature extraction and classification Improves the proposed system performance the proposed system has analysis with the help of OASIS dataset. The proposed results and comparative results shows that the proposed system provides the better results.

scholarly journals An Efficient Classifier for Alzheimer’s Disease Genes Identification

Molecules ◽  
2018 ◽  
Vol 23 (12) ◽  
pp. 3140 ◽  
Author(s):  
Lei Xu ◽  
Guangmin Liang ◽  
Changrui Liao ◽  
Gin-Den Chen ◽  
Chi-Chang Chang
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Early Stage ◽  
Experimental Results ◽  
Support Vector ◽  
Disease Genes ◽  
Sequence Information ◽  
Gene Coding ◽  
Brain Degeneration ◽  
Magnetic Resonance Imaging Mri

Alzheimer’s disease (AD) is considered to one of 10 key diseases leading to death in humans. AD is considered the main cause of brain degeneration, and will lead to dementia. It is beneficial for affected patients to be diagnosed with the disease at an early stage so that efforts to manage the patient can begin as soon as possible. Most existing protocols diagnose AD by way of magnetic resonance imaging (MRI). However, because the size of the images produced is large, existing techniques that employ MRI technology are expensive and time-consuming to perform. With this in mind, in the current study, AD is predicted instead by the use of a support vector machine (SVM) method based on gene-coding protein sequence information. In our proposed method, the frequency of two consecutive amino acids is used to describe the sequence information. The accuracy of the proposed method for identifying AD is 85.7%, which is demonstrated by the obtained experimental results. The experimental results also show that the sequence information of gene-coding proteins can be used to predict AD.

scholarly journals High-throughput screening of natural compounds and inhibition of a major therapeutic target HsGSK-3β for Alzheimer’s disease using computational approaches

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Rohit Shukla ◽  
Tiratha Raj Singh
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
High Throughput Screening ◽  
Complex Disease ◽  
Glycogen Synthase ◽  
Binding Free Energy ◽  
Drug Candidate ◽  
Pharmacokinetic Analysis ◽  
Pharmacokinetic Parameters ◽  
Computational Approaches

Abstract Background Alzheimer’s disease is a leading neurodegenerative disease worldwide and is the 6th leading cause of death in the USA. AD is a very complex disease and the drugs available in the market cannot fully cure it. The glycogen synthase kinase 3 beta plays a major role in the hyperphosphorylation of tau protein which forms the neurofibrillary tangles which is a major hallmark of AD. In this study, we have used a series of computational approaches to find novel inhibitors against GSK-3β to reduce the TAU hyperphosphorylation. Results We have retrieved a set of compounds (n=167,741) and screened against GSK-3β in four sequential steps. The resulting analysis of virtual screening suggested that 404 compounds show good binding affinity and can be employed for pharmacokinetic analysis. From here, we have selected 20 compounds those were good in terms of pharmacokinetic parameters. All these compounds were re-docked by using Autodock Vina followed by Autodock. Four best compounds were employed for MDS and here predicted RMSD, RMSF, Rg, hydrogen bonds, SASA, PCA, and binding-free energy. From all these analyses, we have concluded that out of 167,741 compounds, the ZINC15968620, ZINC15968622, and ZINC70707119 can act as lead compounds against HsGSK-3β to reduce the hyperphosphorylation. Conclusion The study suggested three compounds (ZINC15968620, ZINC15968622, and ZINC70707119) have great potential to be a drug candidate and can be tested using in vitro and in vivo experiments for further characterization and applications.

Sign in / Sign up

Export Citation Format

Share Document