ArticleFrontiers in psychiatry2024
Enhancing Alzheimer's disease diagnosis and staging: a multistage CNN framework using MRI.
Article in Frontiers in psychiatry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning and deep learning for diagnosis of Polyendocrine Metabolic Ovarian Syndrome: systematic review and meta-analysis.Frontiers in endocrinology · 2026Pooled it
- Leveraging pretrained vision transformers for classifying alcohol use disorder using raw resting-state EEG.Neuroimage. Reports · 2026Article
- Artificial intelligence in lumbar radiography: bridging deep learning and clinical practice in low-resource environments.BMJ health & care informatics · 2026Review
- Attention-Based Point Cloud Completion for CT-Equivalent Kidney Volumetry from Tracked Freehand Ultrasound.Journal of imaging informatics in medicine · 2026Article
- Attention Gated-VGG with deep learning-based features for Alzheimer's disease classification.Neurodegenerative disease management · 2026Article
- Convolutional neural networks in brain disease diagnosis: a unified review of Alzheimer's, Parkinson's, and brain tumor classification.Frontiers in neuroscience · 2026Review
- Interpretability and performance of a 3D C-vit model for accurate grading of pediatric brain tumors.Frontiers in oncology · 2026Article
- Automated Alzheimer's disease detection using active learning model with reinforcement learning and scope loss function.Npj mental health research · 2025Article
- Electroencephalography in Autism Spectrum Disorder.Journal of clinical medicine · 2025Review
- Secure Hybrid Deep Learning for MRI-Based Brain Tumor Detection in Smart Medical IoT Systems.Diagnostics (Basel, Switzerland) · 2025Article
- Precise diagnosis of pediatric posterior cranial fossa neoplasms based on 2.5D MRI deep learning.Frontiers in oncology · 2025Article
- A novel deep learning technique for multi classify Alzheimer disease: hyperparameter optimization technique.Frontiers in artificial intelligence · 2025Article
- Integrating Clinical and Histopathological Data to Predict Delayed Graft Function in Kidney Transplant Recipients Using Machine Learning Techniques.Journal of clinical medicine · 2024Article
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Authors and funding
6 authors.
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Abstract
This study addresses the pervasive and debilitating impact of Alzheimer's disease (AD) on individuals and society, emphasizing the crucial need for timely diagnosis. We present a multistage convolutional neural network (CNN)-based framework for AD detection and sub-classification using brain magnetic resonance imaging (MRI). After preprocessing, a 26-layer CNN model was designed to differentiate between healthy individuals and patients with dementia. After detecting dementia, the 26-layer CNN model was reutilized using the concept of transfer learning to further subclassify dementia into mild, moderate, and severe dementia. Leveraging the frozen weights of the developed CNN on correlated medical images facilitated the transfer learning process for sub-classifying dementia classes. An online AD dataset is used to verify the performance of the proposed multistage CNN-based framework. The proposed approach yielded a noteworthy accuracy of 98.24% in identifying dementia classes, whereas it achieved 99.70% accuracy in dementia subclassification. Another dataset was used to further validate the proposed framework, resulting in 100% performance. Comparative evaluations against pre-trained models and the current literature were also conducted, highlighting the usefulness and superiority of the proposed framework and presenting it as a robust and effective AD detection and subclassification method.
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