scholarly journals Corpus Callosum Atrophy in Detection of Mild and Moderate Alzheimer’s Disease Using Brain Magnetic Resonance Image Processing and Machine Learning Techniques

2021 ◽  
pp. 1-18
Author(s):  
Subhrangshu Das ◽  
Priyanka Panigrahi ◽  
Saikat Chakrabarti
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Magnetic Resonance ◽  
Corpus Callosum ◽  
Magnetic Resonance Image ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Techniques ◽  
Brain Magnetic Resonance Image

Background: The total number of people with dementia is projected to reach 82 million in 2030 and 152 in 2050. Early and accurate identification of the underlying causes of dementia, such as Alzheimer’s disease (AD) is of utmost importance. A large body of research has shown that imaging techniques are most promising technologies to improve subclinical and early diagnosis of dementia. Morphological changes, especially atrophy in various structures like cingulate gyri, caudate nucleus, hippocampus, frontotemporal lobe, etc., have been established as markers for AD. Being the largest white matter structure with a high demand of blood supply from several main arterial systems, anatomical alterations of the corpus callosum (CC) may serve as potential indication neurodegenerative disease. Objective: To detect mild and moderate AD using brain magnetic resonance image (MRI) processing and machine learning techniques. Methods: We have performed automatic detection and segmentation of the CC and calculate its morphological features to feed into a multivariate pattern analysis using support vector machine learning techniques. Results: Our results using large patients’ cohort show CC atrophy-based features are capable of distinguishing healthy and mild/moderate AD patients. Our classifiers obtain more than 90%sensitivity and specificity in differentiating demented patients from healthy cohorts and importantly, achieved more than 90%sensitivity and >  80%specificity in detecting mild AD patients. Conclusion: Results from this analysis are encouraging and advocate development of an image analysis software package to detect dementia from brain MRI using morphological alterations of the CC.

scholarly journals Efficient Machine Learning Techniques to Diagnose and Predict Alzheimer’s disease

2020 ◽  
Vol 9 (3) ◽  
pp. 3953-3960
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Early Diagnosis ◽  
Image Data ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Mechanisms ◽  
Learning Techniques

Recent research in computational engineering have evidenced the design and development numerous intelligent models to analyze medical data and derive inferences related to early diagnosis and prediction of disease severity. In this context, prediction and diagnosis of fatal neurodegenerative diseases that comes under the class of dementia from medical image data is considered as the challenging area of research for many researchers. Recently Alzheimer’s disease is considered as major category of dementia that affects major population. Despite of the development of numerous machine learning models for early diagnosis of Alzheimer’s disease, it is observed that there is a lot more scope of research. Addressing the same, this article presents a systematic literature review of machine learning techniques developed for early diagnosis of Alzheimer’s disease. Furthermore this article includes major categories of machine learning algorithms that include artificial neural networks, Support vector machines and Deep learning based ensemble models that helps the budding researchers to explore the scope of research in predicting Alzheimer’s disease. Implementation results depict the comparative analysis of state of art machine learning mechanisms.

MACHINE LEARNING FOR DETECTING EPISTASIS INTERACTIONS AND ITS RELEVANCE TO PERSONALIZED MEDICINE IN ALZHEIMER’S DISEASE: SYSTEMATIC REVIEW

2021 ◽  
pp. 2150047
Author(s):  
Marwa M. Abd El Hamid ◽  
Mohamed Shaheen ◽  
Mai S. Mabrouk ◽  
Yasser M. K. Omar
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Personalized Medicine ◽  
Complex Diseases ◽  
Genetic Data ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Nucleotide Polymorphisms ◽  
Learning Techniques

Alzheimer’s disease (AD) is a progressive disease that attacks the brain’s neurons and causes problems in memory, thinking, and reasoning skills. Personalized Medicine (PM) needs a better and more accurate understanding of the relationship between human genetic data and complex diseases like AD. The goal of PM is to tailor the treatment of a case person to his individual properties. PM requires the prediction of a person’s disease from genetic data, and its success depends on the accurate detection of genetic biomarkers. Single Nucleotide polymorphisms (SNPs) are considered the most prevalent type of variation in the human genome. Epistasis has a biological relevance to complex diseases and has an important impact on PM. Detection of the most significant epistasis interactions associated with complex diseases is a big challenge. This paper reviews several machine learning techniques and algorithms to detect the most significant epistasis interactions in Alzheimer’s disease. We discuss many machine learning techniques that can be used for detecting SNPs’ combinations like Random Forests, Support Vector Machines, Multifactor Dimensionality Reduction, Neural Network, and Deep Learning. This review paper highlights the pros and cons of these techniques and explains how they can be applied in an efficient framework to apply knowledge discovery and data mining in AD disease.

scholarly journals Alzheimer's Disease Early Detection Using Machine Learning Techniques

2021 ◽  
Author(s):  
Roobaea Alroobaea ◽  
Seifeddine Mechti ◽  
Mariem Haoues ◽  
Saeed Rubaiee ◽  
Anas Ahmed ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Support Vector Machine ◽  
Logistic Regression ◽  
Random Forest ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Detection ◽  
Learning Techniques

Abstract Alzheimer's is the main reason for dementia, that affects frequently older adults. This disease is costly especially, in terms of treatment. In addition, Alzheimer's is one of the deaths causes in the old-age citizens. Early Alzheimer's detection helps medical staffs in this disease diagnosis, which will certainly decrease the risk of death. This made the early Alzheimer's disease detection a crucial problem in the healthcare industry. The objective of this research study is to introduce a computer-aided diagnosis system for Alzheimer's disease detection using machine learning techniques. We employed data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the Open Access Series of Imaging Studies (OASIS) brain datasets. Common supervised machine learning techniques have been applied for automatic Alzheimer’s disease detection such as: logistic regression, support vector machine, random forest, linear discriminant analysis, etc. The best accuracy values provided by the machine learning classifiers are 99.43% and 99.10% given by respectively, logistic regression and support vector machine using ADNI dataset, whereas for the OASIS dataset, we obtained 84.33% and 83.92% given by respectively logistic regression and random forest.

Machine Learning Techniques for the Diagnosis of Alzheimer’s Disease

2020 ◽  
Vol 16 (1s) ◽  
pp. 1-35 ◽  
Author(s):  
M. Tanveer ◽  
B. Richhariya ◽  
R. U. Khan ◽  
A. H. Rashid ◽  
P. Khanna ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Machine Learning Techniques ◽  
Learning Techniques

Imaging and machine learning techniques for diagnosis of Alzheimer’s disease

2016 ◽  
Vol 27 (8) ◽  
pp. 857-870 ◽  
Author(s):  
Golrokh Mirzaei ◽  
Anahita Adeli ◽  
Hojjat Adeli
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Probabilistic Neural Network ◽  
Machine Learning Techniques ◽  
Detection Techniques ◽  
The Past ◽  
Learning Techniques ◽  
Diagnosis Accuracy ◽  
Rate Research

AbstractAlzheimer’s disease (AD) is a common health problem in elderly people. There has been considerable research toward the diagnosis and early detection of this disease in the past decade. The sensitivity of biomarkers and the accuracy of the detection techniques have been defined to be the key to an accurate diagnosis. This paper presents a state-of-the-art review of the research performed on the diagnosis of AD based on imaging and machine learning techniques. Different segmentation and machine learning techniques used for the diagnosis of AD are reviewed including thresholding, supervised and unsupervised learning, probabilistic techniques, Atlas-based approaches, and fusion of different image modalities. More recent and powerful classification techniques such as the enhanced probabilistic neural network of Ahmadlou and Adeli should be investigated with the goal of improving the diagnosis accuracy. A combination of different image modalities can help improve the diagnosis accuracy rate. Research is needed on the combination of modalities to discover multi-modal biomarkers.

A Probe into the Parallel Elements of Diabetes Mellitus and Alzheimer’s Disease

2020 ◽  
Vol 17 (8) ◽  
pp. 3598-3604
Author(s):  
M. S. Roobini ◽  
M. Lakshmi
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Morphological Changes ◽  
Memory Loss ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Huge Number ◽  
Learning Techniques ◽  
Vector Machines ◽  
The Mind

Alzheimer’s Disease (AD) is a standout amongst the most familiar types of memory loss influencing a huge number of senior individuals around the world which is the main source of dementia and memory misfortune. AD causes shrinkage in hippocampus and cerebral cortex and it grows the ventricles in the mind Enhancing home and network based composed consideration is basic to alleviating Alzheimer’s impacts on people and families and to decreasing mounting medicinal services costs. Distinguishing early morphological changes in the mind and making early determination are vital for Alzheimer’s ailment (AD). A few machine learning techniques, for example, Support vector machines have been utilized and a portion of these strategies have been appeared to be extremely compelling in diagnosing AD from neuroimages, some of the time significantly more viable than human radiologists. MRI uncover the data of AD however decay districts are diverse for various individuals which makes the finding somewhat trickier. By utilizing Convolutional Neural Networks, the issue can be settled with insignificant mistake rate. This paper proposes a profound Convolutional Neural Network (CNN) for Alzheimer’s Disease finding utilizing mind MRI information examination. The calculation was prepared and tried utilizing the MRI information from Alzheimer’s Disease Neuroimaging Initiative.

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