Classification of Mild Cognitive Impairment Stages Using Machine Learning Methods

Cabrera-León, Ylermi; Báez, Patricio García; Ruiz-Alzola, Juan; Suárez-Araujo, Carmen Paz

doi:10.1109/ines.2018.8523858

Cited by 9 publications

(7 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Artificial neural network approaches. Based on the findings (mainly, that using oversampling and not using the volume of the hippocampus were considered optimal) of their previous research [ 112 ], another work from these authors compared counterpropagation networks (CPNs) with other ML systems [ 113 ]. Class balancing techniques were analyzed by comparing the performance values when the classifiers were trained with the original unbalanced data, with one where an undersampling technique was applied, and with another one balanced with the oversampling method called Synthetic Minority Oversampling Technique (SMOTE) [ 114 ].…”

Section: Resultsmentioning

confidence: 99%

Neural Computation-Based Methods for the Early Diagnosis and Prognosis of Alzheimer’s Disease Not Using Neuroimaging Biomarkers: A Systematic Review

Cabrera-León,

Báez,

Fernández-López

et al. 2024

JAD

View full text Add to dashboard Cite

Background: The growing number of older adults in recent decades has led to more prevalent geriatric diseases, such as strokes and dementia. Therefore, Alzheimer’s disease (AD), as the most common type of dementia, has become more frequent too. Background: Objective: The goals of this work are to present state-of-the-art studies focused on the automatic diagnosis and prognosis of AD and its early stages, mainly mild cognitive impairment, and predicting how the research on this topic may change in the future. Methods: Articles found in the existing literature needed to fulfill several selection criteria. Among others, their classification methods were based on artificial neural networks (ANNs), including deep learning, and data not from brain signals or neuroimaging techniques were used. Considering our selection criteria, 42 articles published in the last decade were finally selected. Results: The most medically significant results are shown. Similar quantities of articles based on shallow and deep ANNs were found. Recurrent neural networks and transformers were common with speech or in longitudinal studies. Convolutional neural networks (CNNs) were popular with gait or combined with others in modular approaches. Above one third of the cross-sectional studies utilized multimodal data. Non-public datasets were frequently used in cross-sectional studies, whereas the opposite in longitudinal ones. The most popular databases were indicated, which will be helpful for future researchers in this field. Conclusions: The introduction of CNNs in the last decade and their superb results with neuroimaging data did not negatively affect the usage of other modalities. In fact, new ones emerged.

show abstract

Section: Resultsmentioning

confidence: 99%

Neural Computation-Based Methods for the Early Diagnosis and Prognosis of Alzheimer’s Disease Not Using Neuroimaging Biomarkers: A Systematic Review

Cabrera-León,

Báez,

Fernández-López

et al. 2024

JAD

View full text Add to dashboard Cite

show abstract

“…Recently, several studies using different approaches have investigated the early diagnosis of AD based on the classification of healthy normal people, patients with MCI, and AD patients [29,49,50,51]. However, to the best of the authors’ knowledge, there are only three studies that have investigated the classification of healthy normal people, EMCI patients, and LMCI patients [52,53,54]. The highest accuracy achieved by Korolev et al was 73% for LMCI versus NC, 67% for LMCI versus EMCI, and 63% for EMCI versus NC.…”

Section: Discussionmentioning

confidence: 99%

A Deep Learning approach for Diagnosis of Mild Cognitive Impairment Based on MRI Images

Gorji

Kaabouch

2019

Brain Sciences

View full text Add to dashboard Cite

Mild cognitive impairment (MCI) is an intermediary stage condition between healthy people and Alzheimer’s disease (AD) patients and other dementias. AD is a progressive and irreversible neurodegenerative disorder, which is a significant threat to people, age 65 and older. Although MCI does not always lead to AD, an early diagnosis at the stage of MCI can be very helpful in identifying people who are at risk of AD. Moreover, the early diagnosis of MCI can lead to more effective treatment, or at least, significantly delay the disease’s progress, and can lead to social and financial benefits. Magnetic resonance imaging (MRI), which has become a significant tool for the diagnosis of MCI and AD, can provide neuropsychological data for analyzing the variance in brain structure and function. MCI is divided into early and late MCI (EMCI and LMCI) and sadly, there is no clear differentiation between the brain structure of healthy people and MCI patients, especially in the EMCI stage. This paper aims to use a deep learning approach, which is one of the most powerful branches of machine learning, to discriminate between healthy people and the two types of MCI groups based on MRI results. The convolutional neural network (CNN) with an efficient architecture was used to extract high-quality features from MRIs to classify people into healthy, EMCI, or LMCI groups. The MRIs of 600 individuals used in this study included 200 control normal (CN) people, 200 EMCI patients, and 200 LMCI patients. This study randomly selected 70 percent of the data to train our model and 30 percent for the test set. The results showed the best overall classification between CN and LMCI groups in the sagittal view with an accuracy of 94.54 percent. In addition, 93.96 percent and 93.00 percent accuracy were reached for the pairs of EMCI/LMCI and CN/EMCI, respectively.

show abstract

“…A similar approach was proposed by Mirzai and Adeli, where supervised learning methods such as SVM, random forest and unsupervised methods such as k-means, hierarchical, fuzzy, spectral, density-based clustering, and Bayesian techniques were analysed [13]. A resampling approach was applied by Cabrera-Leon et al to alleviate class imbalance, and a counter propagation network was compared with an ensemble of nonneural networks for classifying AD [14]. Though Machine learning approaches are used in the majority of AD diagnosis strategies, it suffers from a few limitations like requiring domain knowledge for proper feature selection, and human intervention in segmentation etc., Deep learning techniques, on the other hand, have been used by researchers to improve performance in AD classification using neuroimaging data.…”

Section: Machine Learning Methodsmentioning

confidence: 99%

Performance Analysis of Machine Learning and Deep Learning Models for Classification of Alzheimer’s Disease from Brain MRI

Thayumanasamy¹,

Ramamurthy²

2022

View full text Add to dashboard Cite

Alzheimer's disease (AD) is an irreversible and degenerative brain condition that gradually damages memory and thinking abilities. Despite being incurable, AD causes significant pain and financial hardship to patients and their families. However, medications are most effective when administered early in the course of the disease and early diagnosis is crucial in the treatment of AD to restrict its progression. There are several approaches proposed for computer-assisted AD diagnosis that involve structural and functional imaging modalities, such as sMRI, fMRI, DTI, and PET. Machine learning and deep learning techniques have facilitated the development of novel models for diagnostic accuracy in AD. This research compares the performance of several machine learning and deep convolutional architectures to detect AD from MCI. It is essential to find the effective baseline model for classifying AD, hence all the pre-trained models are evaluated with benchmark dataset. Experimental observations indicate that the DenseNet-169 performed best out of different state-of-the-art architectures, with an average accuracy of 82.2%.

show abstract

Classification of Mild Cognitive Impairment Stages Using Machine Learning Methods

Cited by 9 publications

References 4 publications

Neural Computation-Based Methods for the Early Diagnosis and Prognosis of Alzheimer’s Disease Not Using Neuroimaging Biomarkers: A Systematic Review

Neural Computation-Based Methods for the Early Diagnosis and Prognosis of Alzheimer’s Disease Not Using Neuroimaging Biomarkers: A Systematic Review

A Deep Learning approach for Diagnosis of Mild Cognitive Impairment Based on MRI Images

Performance Analysis of Machine Learning and Deep Learning Models for Classification of Alzheimer’s Disease from Brain MRI

Contact Info

Product

Resources

About