Background: Dementia, with Alzheimer's disease (AD) being the most common type of this neurodegenerative disease, is an under-diagnosed health problem in older people. The creation of classification models based on AD risk factors using Deep Learning is a promising tool to minimize the impact of under-diagnosis.
Objective: To develop a Deep Learning model that uses clinical data from patients with dementia to classify whether they have AD.
Methods: A Deep Learning model to identify AD in clinical records is proposed. In addition, several rebalancing methods have been used to preprocess the dataset and several studies have been carried out to tune up the model.
Results: Model has been tested against other well-established machine learning techniques, having better results than these in terms of AUC with alpha less than 0.05.
Conclusions: The developed Neural Network Model has a good performance and can be an accurate assisting tool for AD diagnosis.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.compbiomed.2023.107814 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Generative Adversarial Networks for Neuroimage Translation.
J Comput Biol
December 2024
Electrical, Computer and Biomedical Engineering, Toronto Metropolitan University, Toronto, Canada.
Image-to-image translation has gained popularity in the medical field to transform images from one domain to another. Medical image synthesis via domain transformation is advantageous in its ability to augment an image dataset where images for a given class are limited. From the learning perspective, this process contributes to the data-oriented robustness of the model by inherently broadening the model's exposure to more diverse visual data and enabling it to learn more generalized features.
View Article and Find Full Text PDFEvaluation of the effectiveness of panoramic radiography in impacted mandibular third molars on deep learning models developed with findings obtained with cone beam computed tomography.
Oral Radiol
December 2024
Department of Oral, Dental and Maxillofacial Radiology, Faculty of Dentistry, Ataturk University, Erzurum, 25240, Turkey.
Objective: The aim of this study is to determine the contact relationship and position of impacted mandibular third molar teeth (IMM) with the mandibular canal (MC) in panoramic radiography (PR) images using deep learning (DL) models trained with the help of cone beam computed tomography (CBCT) and DL to compare the performances of the architectures.
Methods: In this study, a total of 546 IMMs from 290 patients with CBCT and PR images were included. The performances of SqueezeNet, GoogLeNet, and Inception-v3 architectures in solving four problems on two different regions of interest (RoI) were evaluated.
Automated Measurement of Effective Radiation Dose by F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography.
Tomography
December 2024
Department of Nuclear Medicine and Molecular Imaging, Ajou University School of Medicine, Suwon 16499, Republic of Korea.
Background/objectives: Calculating the radiation dose from CT in F-PET/CT examinations poses a significant challenge. The objective of this study is to develop a deep learning-based automated program that standardizes the measurement of radiation doses.
Methods: The torso CT was segmented into six distinct regions using TotalSegmentator.
Noise Reduction in Brain CT: A Comparative Study of Deep Learning and Hybrid Iterative Reconstruction Using Multiple Parameters.
Tomography
December 2024
Department of Diagnostic Radiology, Kitasato University School of Medicine, Sagamihara 252-0374, Japan.
Objectives: We evaluated the noise reduction effects of deep learning reconstruction (DLR) and hybrid iterative reconstruction (HIR) in brain computed tomography (CT).
Methods: CT images of a 16 cm dosimetry phantom, a head phantom, and the brains of 11 patients were reconstructed using filtered backprojection (FBP) and various levels of DLR and HIR. The slice thickness was 5, 2.
CNN-Based Cross-Modality Fusion for Enhanced Breast Cancer Detection Using Mammography and Ultrasound.
Tomography
December 2024
Department of Medical Imaging and Radiological Science, I-Shou University, Kaohsiung City 824005, Taiwan.
Breast cancer is a leading cause of mortality among women in Taiwan and globally. Non-invasive imaging methods, such as mammography and ultrasound, are critical for early detection, yet standalone modalities have limitations in regard to their diagnostic accuracy. This study aims to enhance breast cancer detection through a cross-modality fusion approach combining mammography and ultrasound imaging, using advanced convolutional neural network (CNN) architectures.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!