An improved machine learning technique based on downsized KPCA for Alzheimer's disease classification

Neffati, Syrine; Abdellafou, Khaoula Ben; Jaffel, Ines; Taouali, Okba; Bouzrara, Kais

doi:10.1002/ima.22304

Cited by 29 publications

(15 citation statements)

References 25 publications

(61 reference statements)

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“…ML classifiers can also differentiate between clinical syndromes of frontotemporal dementia (FTD) [219]. Longitudinal studies using feature extraction-based learning techniques provide improved atrophy measures with significantly lower mean absolute error and volumetric markers such as the hippocampus, posterior cingulate cortex and middle temporal gyrus for evaluating disease progression in AD and MCI [190,220,221].…”

Section: Early Diagnosis and Progression To Mci/admentioning

confidence: 99%

Imaging biomarkers in neurodegeneration: current and future practices

et al. 2020

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There is an increasing role for biological markers (biomarkers) in the understanding and diagnosis of neurodegenerative disorders. The application of imaging biomarkers specifically for the in vivo investigation of neurodegenerative disorders has increased substantially over the past decades and continues to provide further benefits both to the diagnosis and understanding of these diseases. This review forms part of a series of articles which stem from the University College London/University of Gothenburg course "Biomarkers in neurodegenerative diseases". In this review, we focus on neuroimaging, specifically positron emission tomography (PET) and magnetic resonance imaging (MRI), giving an overview of the current established practices clinically and in research as well as new techniques being developed. We will also discuss the use of machine learning (ML) techniques within these fields to provide additional insights to early diagnosis and multimodal analysis.

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Section: Early Diagnosis and Progression To Mci/admentioning

confidence: 99%

Imaging biomarkers in neurodegeneration: current and future practices

et al. 2020

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“…Morra et al [23] introduced different models' comparison to detect AD on MRI scans such as SVM and hierarchical AdaBoost models. Neff et al [24] developed an algorithm for feature extraction and reduction by using downsized kernel principal component analysis (DKPCA) and support vector machine (SVM) for AD MRI images. They tested the model on the OASIS datasets and obtained 92.5% accuracy using a multi-support vector machine (MSVM) kernel.…”

Section: Machine Learning-based Techniquementioning

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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“…Deep learning refers to neural networks with a deep number of layers (usually more than five) that extract a hierarchy of features from raw input images. Traditional machine learning algorithms [26][27][28][29][30] extract features manually, whereas deep learning extracts complex, high-level features from the images and trains a large amount of data, thus resulting in greater accuracy. Owing to significantly increased GPU processing power, deep learning methods allow us to train a vast amount of imaging data and increase accuracy despite variations in images.…”

Section: Overview Of Cnn Architecturementioning

confidence: 99%

MRI Segmentation and Classification of Human Brain Using Deep Learning for Diagnosis of Alzheimer’s Disease: A Survey

Yamanakkanavar

Choi

Lee

2020

Sensors

144

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Many neurological diseases and delineating pathological regions have been analyzed, and the anatomical structure of the brain researched with the aid of magnetic resonance imaging (MRI). It is important to identify patients with Alzheimer’s disease (AD) early so that preventative measures can be taken. A detailed analysis of the tissue structures from segmented MRI leads to a more accurate classification of specific brain disorders. Several segmentation methods to diagnose AD have been proposed with varying complexity. Segmentation of the brain structure and classification of AD using deep learning approaches has gained attention as it can provide effective results over a large set of data. Hence, deep learning methods are now preferred over state-of-the-art machine learning methods. We aim to provide an outline of current deep learning-based segmentation approaches for the quantitative analysis of brain MRI for the diagnosis of AD. Here, we report how convolutional neural network architectures are used to analyze the anatomical brain structure and diagnose AD, discuss how brain MRI segmentation improves AD classification, describe the state-of-the-art approaches, and summarize their results using publicly available datasets. Finally, we provide insight into current issues and discuss possible future research directions in building a computer-aided diagnostic system for AD.

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An improved machine learning technique based on downsized KPCA for Alzheimer's disease classification

Cited by 29 publications

References 25 publications

Imaging biomarkers in neurodegeneration: current and future practices

Imaging biomarkers in neurodegeneration: current and future practices

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

MRI Segmentation and Classification of Human Brain Using Deep Learning for Diagnosis of Alzheimer’s Disease: A Survey

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