ArticleDiagnostics (Basel, Switzerland)2023
Accurate Detection of Alzheimer's Disease Using Lightweight Deep Learning Model on MRI Data.
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 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it, 119 citations in OpenAlex.
- Computer-Aided Decision Support Systems of Alzheimer's Disease Diagnosis - A Systematic Review.Current medical imaging · 2025Pooled it
- Effects of transfer learning on seedling detection from drone imagery: a layer freezing study with faster R-CNN.Scientific reports · 2026Article
- AlzhiNet: Traversing from 2D-CNN to 3D-CNN, Towards Early Detection and Diagnosis of Alzheimer's Disease.Interdisciplinary sciences, computational life sciences · 2026Article
- A lightweight CVTC model for accurate Alzheimer's MRI analysis and lesion annotation.NPJ digital medicine · 2026Article
- Detection and Classification of Alzheimer's Disease Using Deep and Machine Learning.Tomography (Ann Arbor, Mich.) · 2025Article
- Secure and fault tolerant cloud based framework for medical image storage and retrieval in a distributed environment.Scientific reports · 2025Article
- A hybrid filtering and deep learning approach for early Alzheimer's disease identification.Scientific reports · 2025Article
- AI-Based Classification of Mild Cognitive Impairment and Cognitively Normal Patients.Journal of clinical medicine · 2025Article
- A hybrid learning approach for MRI-based detection of alzheimer's disease stages using dual CNNs and ensemble classifier.Scientific reports · 2025Article
- A novel deep learning technique for multi classify Alzheimer disease: hyperparameter optimization technique.Frontiers in artificial intelligence · 2025Article
- An MRI based histogram oriented gradient and deep learning approach for accurate classification of mild cognitive impairment and Alzheimer's disease.Frontiers in medicine · 2025Article
- RESIGN: Alzheimer's Disease Detection Using Hybrid Deep Learning based Res-Inception Seg Network.Current Alzheimer research · 2025Article
- MRI-Driven Alzheimer's Disease Diagnosis Using Deep Network Fusion and Optimal Selection of Feature.Bioengineering (Basel, Switzerland) · 2024Article
- A Feature-Fusion Technique-Based Alzheimer's Disease Classification Using Magnetic Resonance Imaging.Diagnostics (Basel, Switzerland) · 2024Article
- Enhancing Alzheimer's disease diagnosis and staging: a multistage CNN framework using MRI.Frontiers in psychiatry · 2024Article
- Advanced interpretable diagnosis of Alzheimer's disease using SECNN-RF framework with explainable AI.Frontiers in artificial intelligence · 2024Article
- A CNN based m5c RNA methylation predictor.Scientific reports · 2023Article
- PFP-HOG: Pyramid and Fixed-Size Patch-Based HOG Technique for Automated Brain Abnormality Classification with MRI.Journal of digital imaging · 2023Article
- Improving Alzheimer's Disease Classification in Brain MRI Images Using a Neural Network Model Enhanced with PCA and SWLDA.Healthcare (Basel, Switzerland) · 2023Article
- DSMRI: Domain Shift Analyzer for Multi-Center MRI Datasets.Diagnostics (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
Abstract
Alzheimer's disease (AD) is a neurodegenerative disorder characterized by cognitive impairment and aberrant protein deposition in the brain. Therefore, the early detection of AD is crucial for the development of effective treatments and interventions, as the disease is more responsive to treatment in its early stages. It is worth mentioning that deep learning techniques have been successfully applied in recent years to a wide range of medical imaging tasks, including the detection of AD. These techniques have the ability to automatically learn and extract features from large datasets, making them well suited for the analysis of complex medical images. In this paper, we propose an improved lightweight deep learning model for the accurate detection of AD from magnetic resonance imaging (MRI) images. Our proposed model achieves high detection performance without the need for deeper layers and eliminates the use of traditional methods such as feature extraction and classification by combining them all into one stage. Furthermore, our proposed method consists of only seven layers, making the system less complex than other previous deep models and less time-consuming to process. We evaluate our proposed model using a publicly available Kaggle dataset, which contains a large number of records in a small dataset size of only 36 Megabytes. Our model achieved an overall accuracy of 99.22% for binary classification and 95.93% for multi-classification tasks, which outperformed other previous models. Our study is the first to combine all methods used in the publicly available Kaggle dataset for AD detection, enabling researchers to work on a dataset with new challenges. Our findings show the effectiveness of our lightweight deep learning framework to achieve high accuracy in the classification of 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.