ArticleDiagnostics (Basel, Switzerland)2023
Alzheimer Disease Classification through Transfer Learning Approach.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline 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
- Deep learning-based cognitive impairment brain imaging analysis: New methods, new technologies, and new paradigms.Neural regeneration research · 2026Article
- A Deep Learning-Based EffConvNeXt Model for Automatic Classification of Cystic Bronchiectasis: An Explainable AI Approach.Journal of imaging informatics in medicine · 2026Article
- FuzzyFed-CNN: secure and explainable multimodal federated learning for early Alzheimer's diagnosis.Frontiers in artificial intelligence · 2026Article
- Alzheimer's disease detection using a quantum deep neural network with Haralick feature extraction and simulated annealing optimization.PeerJ. Computer science · 2026Article
- NeuroNet-AD: A Multimodal Deep Learning Framework for Multiclass Alzheimer's Disease Diagnosis.Bioengineering (Basel, Switzerland) · 2025Article
- Enhancing Diagnostic Accuracy of Neurological Disorders Through Feature-Driven Multi-Class Classification with Machine Learning.Diagnostics (Basel, Switzerland) · 2025Article
- Intelligent Alzheimer's diagnosis and disability assessment: robust medical imaging analysis using ensemble learning with ResNet-50 and EfficientNet-B3.Frontiers in medicine · 2025Article
- Biologically inspired hybrid model for Alzheimer's disease classification using structural MRI in the ADNI dataset.Frontiers in artificial intelligence · 2025Article
- Novel deep learning for multi-class classification of Alzheimer's in disability using MRI datasets.Frontiers in bioinformatics · 2025Article
- Machine Learning-Driven GLCM Analysis of Structural MRI for Alzheimer's Disease Diagnosis.Bioengineering (Basel, Switzerland) · 2024Article
- Fractional gradient optimized explainable convolutional neural network for Alzheimer's disease diagnosis.Heliyon · 2024Article
- Residual-Based Multi-Stage Deep Learning Framework for Computer-Aided Alzheimer's Disease Detection.Journal of imaging · 2024Article
- DenseIncepS115: a novel network-level fusion framework for Alzheimer's disease prediction using MRI images.Frontiers in oncology · 2024Article
- A combinatorial deep learning method for Alzheimer's disease classification-based merging pretrained networks.Frontiers in computational neuroscience · 2024Article
- Survey of Transfer Learning Approaches in the Machine Learning of Digital Health Sensing Data.Journal of personalized medicine · 2023Review
- Improving Alzheimer's Disease Classification in Brain MRI Images Using a Neural Network Model Enhanced with PCA and SWLDA.Healthcare (Basel, Switzerland) · 2023Article
- Skin Lesion Segmentation Using an Ensemble of Different Image Processing Methods.Diagnostics (Basel, Switzerland) · 2023Article
- Improved transfer learning using textural features conflation and dynamically fine-tuned layers.PeerJ. Computer science · 2023Article
Corrections and comments
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Alzheimer's disease (AD) is a slow neurological disorder that destroys the thought process, and consciousness, of a human. It directly affects the development of mental ability and neurocognitive functionality. The number of patients with Alzheimer's disease is increasing day by day, especially in old aged people, who are above 60 years of age, and, gradually, it becomes cause of their death. In this research, we discuss the segmentation and classification of the Magnetic resonance imaging (MRI) of Alzheimer's disease, through the concept of transfer learning and customizing of the convolutional neural network (CNN) by specifically using images that are segmented by the Gray Matter (GM) of the brain. Instead of training and computing the proposed model accuracy from the start, we used a pre-trained deep learning model as our base model, and, after that, transfer learning was applied. The accuracy of the proposed model was tested over a different number of epochs, 10, 25, and 50. The overall accuracy of the proposed model was 97.84%.
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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.