ArticleBrain sciences2019
A Deep Learning approach for Diagnosis of Mild Cognitive Impairment Based on MRI Images.
Article in Brain sciences, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 2 of them syntheses that pooled it.
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Who cites it
28 citing papers in PubMed, 2 syntheses or guidelines pooled it, 94 citations in OpenAlex.
- Predictive modelling of brain disorders with magnetic resonance imaging: A systematic review of modelling practices, transparency, and interpretability in the use of convolutional neural networks.Human brain mapping · 2023Pooled it
- An Exploration: Alzheimer's Disease Classification Based on Convolutional Neural Network.BioMed research international · 2022Pooled it
- Comparative analysis of multiple deep learning models with mitigation-driven approaches for enhanced Alzheimer's disease classification.Scientific reports · 2025Article
- A fine-tuned convolutional neural network model for accurate Alzheimer's disease classification.Scientific reports · 2025Article
- Multi-model deep learning for dementia detection: addressing data and model limitations.Frontiers in neuroscience · 2025Article
- A Deep Learning-Based Ensemble Method for Early Diagnosis of Alzheimer's Disease using MRI Images.Neuroinformatics · 2024Article
- Predicting Four-Year's Alzheimer's Disease Onset Using Longitudinal Neurocognitive Tests and MRI Data Using Explainable Deep Convolutional Neural Networks.Journal of Alzheimer's disease : JAD · 2024Article
- Enhanced neurological anomaly detection in MRI images using deep convolutional neural networks.Frontiers in medicine · 2024Article
- Machine learning-based prediction of mild cognitive impairment among individuals with normal cognitive function.Frontiers in neurology · 2024Article
- Application of machine learning in dementia diagnosis: A systematic literature review.Heliyon · 2023Article
- Optimized Convolutional Fusion for Multimodal Neuroimaging in Alzheimer's Disease Diagnosis: Enhancing Data Integration and Feature Extraction.Journal of personalized medicine · 2023Article
- Identifying healthy individuals with Alzheimer's disease neuroimaging phenotypes in the UK Biobank.Communications medicine · 2023Article
- Application of image processing and transfer learning for the detection of rust disease.Scientific reports · 2023Article
- Harnessing the potential of machine learning and artificial intelligence for dementia research.Brain informatics · 2023Review
- Voxel-based morphometry and a deep learning model for the diagnosis of early Alzheimer's disease based on cerebral gray matter changes.Cerebral cortex (New York, N.Y. : 1991) · 2023Article
- A new strategy for the early detection of alzheimer disease stages using multifractal geometry analysis based on K-Nearest Neighbor algorithm.Scientific reports · 2022Article
- Patterns of structure-function association in normal aging and in Alzheimer's disease: Screening for mild cognitive impairment and dementia with ML regression and classification models.Frontiers in aging neuroscience · 2022Article
- A CAD System for Alzheimer's Disease Classification Using Neuroimaging MRI 2D Slices.Computational and mathematical methods in medicine · 2022Article
- Analysis of Features of Alzheimer's Disease: Detection of Early Stage from Functional Brain Changes in Magnetic Resonance Images Using a Finetuned ResNet18 Network.Diagnostics (Basel, Switzerland) · 2021Article
- Information Flow Pattern in Early Mild Cognitive Impairment Patients.Frontiers in neurology · 2021Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.