ArticleFrontiers in public health2022
Evaluation of Neuro Images for the Diagnosis of Alzheimer's Disease Using Deep Learning Neural Network.
Article in Frontiers in public health, 2022. 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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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it, 109 citations in OpenAlex.
- Pooled it
- MRI neuroimaging-based Alzheimer's disease stage classification using deep neural network with convolutional block attention module and GAN-style noise injection.Scientific reports · 2026Article
- GGDA-net: geometry-guided deformable attention network for Alzheimer's disease image classification.Frontiers in neuroscience · 2026Article
- Enhanced gastrointestinal disease classification using a convvit hybrid model on endoscopic images.Physical and engineering sciences in medicine · 2025Article
- Multi-Target Drug Design in Alzheimer's Disease Treatment: Emerging Technologies, Advantages, Challenges, and Limitations.Pharmacology research & perspectives · 2025Review
- Recent Advancements in Neuroimaging-Based Alzheimer's Disease Prediction Using Deep Learning Approaches in e-Health: A Systematic Review.Health science reports · 2025Review
- RESIGN: Alzheimer's Disease Detection Using Hybrid Deep Learning based Res-Inception Seg Network.Current Alzheimer research · 2025Article
- An Evolutionary Federated Learning Approach to Diagnose Alzheimer's Disease Under Uncertainty.Diagnostics (Basel, Switzerland) · 2025Article
- Enhancing early detection of Alzheimer's disease through hybrid models based on feature fusion of multi-CNN and handcrafted features.Scientific reports · 2024Article
- Article
- Alzheimer Disease Detection Studies: Perspective on Multi-Modal Data.Yearbook of medical informatics · 2024Review
- Navigating the Alzheimer's Treatment Landscape: Unraveling Amyloid-beta Complexities and Pioneering Precision Medicine Approaches.Current topics in medicinal chemistry · 2024Review
- DenseIncepS115: a novel network-level fusion framework for Alzheimer's disease prediction using MRI images.Frontiers in oncology · 2024Article
- Deep Learning-Based Feature Extraction from Whole-Body PET/CT Employing Maximum Intensity Projection Images: Preliminary Results of Lung Cancer Data.Nuclear medicine and molecular imaging · 2023Article
- Medical Image Despeckling Using the Invertible Sparse Fuzzy Wavelet Transform with Nature-Inspired Minibatch Water Wave Swarm Optimization.Diagnostics (Basel, Switzerland) · 2023Article
- Deep Learning Aided Neuroimaging and Brain Regulation.Sensors (Basel, Switzerland) · 2023Review
- Automated Medical Diagnosis of Alzheimer´s Disease Using an Efficient Net Convolutional Neural Network.Journal of medical systems · 2023Article
- A smart IoMT based architecture for E-healthcare patient monitoring system using artificial intelligence algorithms.Frontiers in physiology · 2023Article
- Broad learning for early diagnosis of Alzheimer's disease using FDG-PET of the brain.Frontiers in neuroscience · 2023Article
Corrections and comments
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Authors and funding
6 authors at 5 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Alzheimer's Disease (AD) is a progressive, neurodegenerative brain disease and is an incurable ailment. No drug exists for AD, but its progression can be delayed if the disorder is identified at its initial stage. Therefore, an early analysis of AD is of fundamental importance for patient care and efficient treatment. Neuroimaging techniques aim to assist the physician in the diagnosis of brain disorders by using images. Positron emission tomography (PET) is a kind of neuroimaging technique employed to create 3D images of the brain. Due to many PET images, researchers attempted to develop computer-aided diagnosis (CAD) to differentiate normal control from AD. Most of the earlier methods used image processing techniques for preprocessing and attributes extraction and then developed a model or classifier to classify the brain images. As a result, the retrieved features had a significant impact on the recognition rate of previous techniques. A novel and enhanced CAD system based on a convolutional neural network (CNN) is formulated to address this issue, capable of discriminating normal control from Alzheimer's disease patients. The proposed approach is evaluated using the 18FDG-PET images of 855 patients, including 635 normal control and 220 Alzheimer's disease patients from the ADNI database. The result showed that the proposed CAD system yields an accuracy of 96%, a sensitivity of 96%, and a specificity of 94%, leading to splendid performance when related to the methods already in use that are specified in the literature.
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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 Socratic graph.