ReviewSensors (Basel, Switzerland)2020
MRI Segmentation and Classification of Human Brain Using Deep Learning for Diagnosis of Alzheimer's Disease: A Survey.
Review in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
47 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning in CT image segmentation of cervical cancer: a systematic review and meta-analysis.Radiation oncology (London, England) · 2022Pooled it
- A Prediction Model for Cognitive Impairment Risk in Colorectal Cancer after Chemotherapy Treatment.BioMed research international · 2021Trial
- A Class-Wise Deep Ensemble Framework Using ResNet101 and DenseNet201 for Brain Tumor Classification.Journal of imaging informatics in medicine · 2026Article
- A multi-task segFormer framework for lesion segmentation and cerebral palsy classification based on multi-modal MRI in infant with periventricular white matter injury.Frontiers in neuroscience · 2026Article
- Cancer and Aging Biomarkers: Classification, Early Detection Technologies and Emerging Research Trends.Biosensors · 2025Review
- Segmentation-Guided Development of Visual Classification Criteria for Alzheimer's Disease.medRxiv : the preprint server for health sciences · 2025Article
- Brain Anatomy Prior Modeling to Forecast Clinical Progression of Cognitive Impairment with Structural MRI.Pattern recognition · 2025Article
- Efficient 3D Biomedical Image Segmentation by Parallelly Multiscale Transformer-CNN Aggregation Network.Chemical & biomedical imaging · 2025Article
- Classifying and diagnosing Alzheimer's disease with deep learning using 6735 brain MRI images.Scientific reports · 2025Article
- Identification of Bipolar Disorder and Schizophrenia Based on Brain CT and Deep Learning Methods.Journal of imaging informatics in medicine · 2025Article
- A Case for Automated Segmentation of MRI Data in Neurodegenerative Diseases: Type II GM1 Gangliosidosis.NeuroSci · 2025Article
- Quantitative magnetic resonance imaging in Alzheimer's disease: a narrative review.Quantitative imaging in medicine and surgery · 2025Review
- Alzheimer's Prediction Methods with Harris Hawks Optimization (HHO) and Deep Learning-Based Approach Using an MLP-LSTM Hybrid Network.Diagnostics (Basel, Switzerland) · 2025Article
- A mini review of transforming dementia care in China with data-driven insights: overcoming diagnostic and time-delayed barriers.Frontiers in aging neuroscience · 2025Review
- Skin-lesion segmentation using boundary-aware segmentation network and classification based on a mixture of convolutional and transformer neural networks.Frontiers in medicine · 2025Article
- Exploring Multi-Pathology Brain Segmentation: From Volume-Based to Component-Based Deep Learning Analysis.Journal of imaging · 2024Article
- Diagnostic Models for Differentiating COVID-19-Related Acute Ischemic Stroke Using Machine Learning Methods.Diagnostics (Basel, Switzerland) · 2024Article
- A Feature-Fusion Technique-Based Alzheimer's Disease Classification Using Magnetic Resonance Imaging.Diagnostics (Basel, Switzerland) · 2024Article
- Assistive tools for classifying neurological disorders using fMRI and deep learning: A guide and example.Brain and behavior · 2024Article
- NnU-Net versus mesh growing algorithm as a tool for the robust and timely segmentation of neurosurgical 3D images in contrast-enhanced T1 MRI scans.Acta neurochirurgica · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
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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Registered trials
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.