Data Imputation of Brain MRI Features with Enhanced Multinomial Logistic Regression for Alzheimer's Disease Classification

2021 ◽  
Author(s):  
Naganandhini ◽  
Pichai Shanmugavadivu ◽  
Kanimozhi ◽  
Muthu Subash Kavitha
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Logistic Regression ◽  
Multinomial Logistic Regression ◽  
Brain Mri ◽  
Disease Classification ◽  
Data Imputation ◽  
Mri Features

scholarly journals Alzheimer’s Disease: Classification and Detection using MRI Dataset

2021 ◽  
Vol 10 (5) ◽  
pp. 70-72
Author(s):  
V P Suhaira ◽  
Sita S ◽  
Joby George
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Heart Function ◽  
Brain Mri ◽  
Memory Loss ◽  
Disease Classification ◽  
Data Set ◽  
Mri Scans ◽  
Disease Experience ◽  
The Brain

Alzheimer's disease (AD) is a hereditary brain condition that is incurable and progresses over time. Patients with Alzheimer's disease experience memory loss, uncertainty, and difficulty speaking, reading, and writing as a result of this condition. Alzheimer's disease eventually affects the portion of the brain that controls breathing and heart function, leading to death. This framework proposes the OASIS (Open Access Series of Imaging Studies) dataset, which contains the existing MRI data set, which is comprised of a longitudinal sample of 150 subjects aged 60 to 96 who were all acquired on the same scanner using similar sequences. This paper uses a combination of brain MRI scans and psychological parameters to predict disease with high accuracy using various classifier algorithms, and the results can be compared to improve performance.

scholarly journals Alzheimer's Disease Classification Accuracy is Improved by MRI Harmonization based on Attention-Guided Generative Adversarial Networks

2021 ◽  
Author(s):  
surabhi sinha ◽  
Sophia I. Thomopoulos ◽  
Pradeep Lam ◽  
Alexandra Muir ◽  
Paul M. Thompson
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Domain Adaptation ◽  
Imaging Techniques ◽  
Brain Mri ◽  
Memory Loss ◽  
Disease Classification ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Network

Alzheimer's disease (AD) accounts for 60% of dementia cases worldwide; patients with the disease typically suffer from irreversible memory loss and progressive decline in multiple cognitive domains. With brain imaging techniques such as magnetic resonance imaging (MRI), microscopic brain changes are detectable even before abnormal memory loss is detected clinically. Patterns of brain atrophy can be measured using MRI, which gives us an opportunity to facilitate AD detection using image classification techniques. Even so, MRI scanning protocols and scanners differ across studies. The resulting differences in image contrast and signal to noise make it important to train and test classification models on multiple datasets, and to handle shifts in image characteristics across protocols (also known as domain transfer or domain adaptation). Here, we examined whether adversarial domain adaptation can boost the performance of a Convolutional Neural Network (CNN) model designed to classify AD. To test this, we used an Attention-Guided Generative Adversarial Network (GAN) to harmonize images from three publicly available brain MRI datasets - ADNI, AIBL and OASIS - adjusting for scanner-dependent effects. Our AG-GAN optimized a joint objective function that included attention loss, pixel loss, cycle-consistency loss and adversarial loss; the model was trained bidirectionally in an end-to-end fashion. For AD classification, we adapted the popular 2D AlexNet CNN to handle 3D images. Classification based on harmonized MR images significantly outperformed classification based on the three datasets in non-harmonized form, motivating further work on image harmonization using adversarial techniques.

scholarly journals Improved Alzheimer’s Disease Detection by MRI Using Multimodal Machine Learning Algorithms

Diagnostics ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 2103
Author(s):  
Gopi Battineni ◽  
Mohmmad Amran Hossain ◽  
Nalini Chintalapudi ◽  
Enea Traini ◽  
Venkata Rao Dhulipalla ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Brain Mri ◽  
Modern Medicine ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Adult Onset ◽  
Mri Features ◽  
Supervised Classifiers

Adult-onset dementia disorders represent a challenge for modern medicine. Alzheimer’s disease (AD) represents the most diffused form of adult-onset dementias. For half a century, the diagnosis of AD was based on clinical and exclusion criteria, with an accuracy of 85%, which did not allow for a definitive diagnosis, which could only be confirmed by post-mortem evaluation. Machine learning research applied to Magnetic Resonance Imaging (MRI) techniques can contribute to a faster diagnosis of AD and may contribute to predicting the evolution of the disease. It was also possible to predict individual dementia of older adults with AD screening data and ML classifiers. To predict the AD subject status, the MRI demographic information and pre-existing conditions of the patient can help to enhance the classifier performance. In this work, we proposed a framework based on supervised learning classifiers in the dementia subject categorization as either AD or non-AD based on longitudinal brain MRI features. Six different supervised classifiers are incorporated for the classification of AD subjects and results mentioned that the gradient boosting algorithm outperforms other models with 97.58% of accuracy.

scholarly journals Alzheimer’s Disease Classification using Leung-Malik Filtered Bank Features and Weak Classifier

2019 ◽  
Vol 8 (3) ◽  
pp. 1956-1961
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Filter Bank ◽  
Binary Classification ◽  
Brain Mri ◽  
Texture Features ◽  
Disease Classification ◽  
Weak Classifier ◽  
Frame Work ◽  
Multi Class Classification

We propose a frame work to classify Brain MRI images in to Alzheimer’s Disease (AD), Cognitive Normal (CN) and Mild Cognitive impairments (MCI). We use 114No’s of T2 weighted MRI Volumes. We extracted relative texture features from Leung-Malik Filter bank, k means is used to generate Bag of Dictionary (BoD) from LM Filtered images. We performed binary classification and Multi class Classification using different Classifiers, Adaboost Classifier gives better performance both in binary and multi class classifications in comparison with other classifiers. Performance of proposed system is enhanced than compared to the existing techniques. It has Sensitivity for AD-CN 89.8, AD-MCI 78.82, AD-CN-MCI 77.77, Specificity for AD-CN79.22, AD-MCI 80.00, AD-CN-MCI 58.88, and positive prediction value for AD-CN 79.48, AD-MCI 83.75, AD-CN-MCI 68.47 and Accuracy AD-CN 84.24, AD-MCI 79.33, AD-CN-MCI 72.88.

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