Hybrid Feature Extraction and Classification for Alzheimer’s Disease Detection

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.

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

Computational and Mathematical Methods in Medicine ◽

10.1155/2021/5514839 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Morteza Amini ◽

MirMohsen Pedram ◽

AliReza Moradi ◽

Mahshad Ouchani

Keyword(s):

Neural Network ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Feature Extraction ◽

Convolutional Neural Network ◽

Nearest Neighbor ◽

Learning Algorithm ◽

Early Stage ◽

Support Vector ◽

Feature Extraction Method

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

Applied System Innovation ◽

10.3390/asi4030049 ◽

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.

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Hybrid Feature Extraction Technique on Brain MRI Images for Content-Based Image Retrieval of Alzheimer’s Disease

Lecture Notes in Electrical Engineering - Advances in Communication, Signal Processing, VLSI, and Embedded Systems ◽

10.1007/978-981-15-0626-0_11 ◽

2019 ◽

pp. 127-141

Author(s):

K. Chethan ◽

Rekha Bhandarkar

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Feature Extraction ◽

Image Retrieval ◽

Brain Mri ◽

Content Based Image Retrieval ◽

Extraction Technique ◽

Hybrid Feature Extraction ◽

Feature Extraction Technique

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Empirical Mode Decomposition as a feature extraction method for Alzheimer's Disease Diagnosis

2012 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC) ◽

10.1109/nssmic.2012.6551897 ◽

2012 ◽

Cited By ~ 1

Author(s):

A. Rojas ◽

J. M. Gorriz ◽

J. Ramirez ◽

A. Gallix ◽

I. A. Illan

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Feature Extraction ◽

Empirical Mode Decomposition ◽

Extraction Method ◽

Disease Diagnosis ◽

Feature Extraction Method ◽

Mode Decomposition ◽

Alzheimer’S Disease Diagnosis

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Variational Feature Extraction of Two-Dimensional Variational Mode Decomposition for Alzheimer’s Disease Classification

2020 12th International Conference on Information Technology and Electrical Engineering (ICITEE) ◽

10.1109/icitee49829.2020.9271761 ◽

2020 ◽

Author(s):

Ungsumalee Suttapakti ◽

Peerasak Pianprasit

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Feature Extraction ◽

Disease Classification ◽

Two Dimensional ◽

Variational Mode Decomposition ◽

Mode Decomposition

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Applying Artificial Intelligence Techniques for Prediction of Neurodegenerative Disorders: A Comparative Case-Study on Clinical Tests and Neuroimaging Tests with Alzheimer’s Disease

10.20944/preprints202003.0299.v1 ◽

2020 ◽

Author(s):

Ahmed Abdullah Farid ◽

Gamal Selim ◽

Hatem Khater

Keyword(s):

Alzheimer’S Disease ◽

Data Mining ◽

Alzheimer's Disease ◽

Feature Extraction ◽

Early Stage ◽

Support Vector ◽

Clinical Dataset ◽

Proposed Model ◽

Hybrid Classification ◽

Medical Dataset

Alzheimer's disease (AD) detection acting as an essential role in global health care due to misdiagnosis and sharing many clinical sets with other types of dementia, and costly monitoring the progression of the disease over time by magnetic reasoning imaging (MRI) with consideration of human error in manual reading. This paper goal a comparative study on the performance of data mining techniques on two datasets of Clinical and Neuroimaging Tests with AD. Our proposed model in the first stage, Apply clinical medical dataset to a composite hybrid feature selection (CHFS), for extract new features to select the best features due to eliminating obscures features, In parallel with Apply a novel hybrid feature extraction of three batch edge detection algorithm and texture from MRI images dataset and optimized with fuzzy 64-bin histogram. In the second stage, we applied a clinical dataset to a stacked hybrid classification(SHC) model to combine Jrip and random forest classifiers with six model evaluations as meta-classifier individually to improve the prediction of clinical diagnosis. At the same stage of improving the classification accuracy of neuroimaging (MRI) dataset images by applying a convolution neural network (CNN) in comparison with traditional classifiers, running on extracted features from images. The authors have collected the clinical dataset of 426 subjects with (1229 potential patient sample) from oasis.org and (MRI) dataset from a benchmark kaggle.com with a total of around ~5000 images each segregated into the severity of Alzheimer's. The datasets evaluated using an explorer set of weka data mining software for the analysis purpose. The experimental show that the proposed model of &rlm;(CHFS) feature extraction &rlm; lead to effectively reduced the false-negative rate with a relatively high overall accuracy with a stack hybrid classification of support vector machine (SVM) as meta-classifier of 96.50% compared to 68.83% of the previous result on a clinical dataset, Besides a compared model of CNN classification on MRI images dataset of 80.21%. The results showed the superiority of our CHFS model in predicting Alzheimer's disease more accurately with the clinical medical dataset in early-stage compared with the neuroimaging (MRI) dataset. The results of the proposed model were able to predict with accurately classify Alzheimer's clinical samples at a low cost in comparison with the MRI-CNN images model at the early stage and get a good indicator for high classification rate for MRI images when applying our proposed model of SHC.

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

Jurnal Matematika MANTIK ◽

10.15642/mantik.2018.4.2.83-89 ◽

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%.

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