ArticleJournal of medical systems2019
Automated Detection of Alzheimer's Disease Using Brain MRI Images- A Study with Various Feature Extraction Techniques.
Article in Journal of medical systems, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 1 of them a synthesis that pooled it.
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Who cites it
45 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
- Hippocampal surface analysis using discrete Ricci flow and Riemannian metrics for the diagnosis of Alzheimer's disease.Research square · 2026Article
- NCA-EVA: An Innovative Ensemble-Based Approach for Alzheimer's Disease Detection from Magnetic Resonance Imaging.Journal of imaging informatics in medicine · 2026Article
- Diagnosis and Prediction of Alzheimer's Disease via a High-Level Convolutional Block Attention Module-Residual Network.Interdisciplinary sciences, computational life sciences · 2026Article
- A hybrid deep learning approach integrating CNN and transformer for lung cancer classification using CT scans.Scientific reports · 2026Article
- An optimized hybrid deep learning model to detect Alzheimer disease.Scientific reports · 2025Article
- A dual-model AI framework for Alzheimer's disease diagnosis using clinical and MRI data.Frontiers in medicine · 2025Article
- Multi-model machine learning for automated identification of rice diseases using leaf image data.PloS one · 2025Article
- Early diagnosis of Alzheimer's disease and mild cognitive impairment using MRI analysis and machine learning algorithms.Discover applied sciences · 2025Article
- A modified deep learning method for Alzheimer's disease detection based on the facial submicroscopic features in mice.Biomedical engineering online · 2024Article
- A Feature-Fusion Technique-Based Alzheimer's Disease Classification Using Magnetic Resonance Imaging.Diagnostics (Basel, Switzerland) · 2024Article
- Lattice 123 pattern for automated Alzheimer's detection using EEG signal.Cognitive neurodynamics · 2024Article
- Secondary Bioactive Metabolites from Foods of Plant Origin asFoods (Basel, Switzerland) · 2024Review
- PFP-HOG: Pyramid and Fixed-Size Patch-Based HOG Technique for Automated Brain Abnormality Classification with MRI.Journal of digital imaging · 2023Article
- CAD-ALZ: A Blockwise Fine-Tuning Strategy on Convolutional Model and Random Forest Classifier for Recognition of Multistage Alzheimer's Disease.Diagnostics (Basel, Switzerland) · 2023Article
- Advanced Diagnostic Technique for Alzheimer's Disease using MRI Top-Ranked Volume and Surface-based Features.Journal of biomedical physics & engineering · 2022Article
- Automatic Detection of Tuberculosis Using VGG19 with Seagull-Algorithm.Life (Basel, Switzerland) · 2022Article
- A Two-stream Convolutional Network for Musculoskeletal and Neurological Disorders Prediction.Journal of medical systems · 2022Article
- HTLML: Hybrid AI Based Model for Detection of Alzheimer's Disease.Diagnostics (Basel, Switzerland) · 2022Article
- Deep Learning-Based Diagnosis of Alzheimer's Disease.Journal of personalized medicine · 2022Review
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The aim of this work is to develop a Computer-Aided-Brain-Diagnosis (CABD) system that can determine if a brain scan shows signs of Alzheimer's disease. The method utilizes Magnetic Resonance Imaging (MRI) for classification with several feature extraction techniques. MRI is a non-invasive procedure, widely adopted in hospitals to examine cognitive abnormalities. Images are acquired using the T2 imaging sequence. The paradigm consists of a series of quantitative techniques: filtering, feature extraction, Student's t-test based feature selection, and k-Nearest Neighbor (KNN) based classification. Additionally, a comparative analysis is done by implementing other feature extraction procedures that are described in the literature. Our findings suggest that the Shearlet Transform (ST) feature extraction technique offers improved results for Alzheimer's diagnosis as compared to alternative methods. The proposed CABD tool with the ST + KNN technique provided accuracy of 94.54%, precision of 88.33%, sensitivity of 96.30% and specificity of 93.64%. Furthermore, this tool also offered an accuracy, precision, sensitivity and specificity of 98.48%, 100%, 96.97% and 100%, respectively, with the benchmark MRI database.
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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.