ArticleJournal of digital imaging2020
Binary Classification of Alzheimer's Disease Using sMRI Imaging Modality and Deep Learning.
Article in Journal of digital imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 2 of them syntheses that pooled it.
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Who cites it
31 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- From Single-Modal to Multi-Modal Artificial Intelligence in Alzheimer's Disease: A Systematic Review of Databases, Modalities, Diagnostic Performance, and Clinical Translation Challenges.Sensors (Basel, Switzerland) · 2026Pooled it
- 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
- Integrating anatomical priors and clinical semantics for MRI-based diagnosis and care support in Alzheimer's disease.Frontiers in neuroscience · 2026Article
- A vision-language foundation model for Alzheimer's disease diagnosis using MRI and clinical data.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- A Convolutional Mixer-Based Deep Learning Network for Alzheimer's Disease Classification from Structural Magnetic Resonance Imaging.Diagnostics (Basel, Switzerland) · 2025Article
- Stacked CNN-based multichannel attention networks for Alzheimer disease detection.Scientific reports · 2025Article
- Article
- Comorbidity-based framework for Alzheimer's disease classification using graph neural networks.Scientific reports · 2024Article
- Detection of hypoplastic left heart syndrome anatomy from cardiovascular magnetic resonance images using machine learning.Magma (New York, N.Y.) · 2024Article
- Voxel-wise Fusion of Resting fMRI Networks and Gray Matter Volume for Alzheimer's Disease Classification using Deep Multimodal Learning.Research square · 2023Article
- An Efficient Ensemble Approach for Alzheimer's Disease Detection Using an Adaptive Synthetic Technique and Deep Learning.Diagnostics (Basel, Switzerland) · 2023Article
- XGBoost-SHAP-based interpretable diagnostic framework for alzheimer's disease.BMC medical informatics and decision making · 2023Article
- Breast Tumor Classification in Ultrasound Images by Fusion of Deep Convolutional Neural Network and Shallow LBP Feature.Journal of digital imaging · 2023Article
- Automatic Detection of Alzheimer's Disease using Deep Learning Models and Neuro-Imaging: Current Trends and Future Perspectives.Neuroinformatics · 2023Review
- Alzheimer Disease Classification through Transfer Learning Approach.Diagnostics (Basel, Switzerland) · 2023Article
- Deep belief network-based approach for detecting Alzheimer's disease using the multi-omics data.Computational and structural biotechnology journal · 2023Article
- DAD-Net: Classification of Alzheimer's Disease Using ADASYN Oversampling Technique and Optimized Neural Network.Molecules (Basel, Switzerland) · 2022Article
- Prediction of Alzheimer's Disease by a Novel Image-Based Representation of Gene Expression.Genes · 2022Article
- A Noise-robust and Overshoot-free Alternative to Unsharp Masking for Enhancing the Acuity of MR Images.Journal of digital imaging · 2022Article
- Early-Stage Alzheimer's Disease Categorization Using PET Neuroimaging Modality and Convolutional Neural Networks in the 2D and 3D Domains.Sensors (Basel, Switzerland) · 2022Article
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3 authors.
Funding
Abstract
Alzheimer's disease (AD) is an irreversible devastative neurodegenerative disorder associated with progressive impairment of memory and cognitive functions. Its early diagnosis is crucial for the development of possible future treatment option(s). Structural magnetic resonance images (sMRI) play an important role to help in understanding the anatomical changes related to AD especially in its early stages. Conventional methods require the expertise of domain experts and extract hand-picked features such as gray matter substructures and train a classifier to distinguish AD subjects from healthy subjects. Different from these methods, this paper proposes to construct multiple deep 2D convolutional neural networks (2D-CNNs) to learn the various features from local brain images which are combined to make the final classification for AD diagnosis. The whole brain image was passed through two transfer learning architectures; Inception version 3 and Xception, as well as a custom Convolutional Neural Network (CNN) built with the help of separable convolutional layers which can automatically learn the generic features from imaging data for classification. Our study is conducted using cross-sectional T1-weighted structural MRI brain images from Open Access Series of Imaging Studies (OASIS) database to maintain the size and contrast over different MRI scans. Experimental results show that the transfer learning approaches exceed the performance of non-transfer learning-based approaches demonstrating the effectiveness of these approaches for the binary AD classification task.
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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.