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      Evaluation of Neuro Images for the Diagnosis of Alzheimer's Disease Using Deep Learning Neural Network


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          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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          2020 Alzheimer's disease facts and figures

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            COVID-19 Patient Health Prediction Using Boosted Random Forest Algorithm

            Integration of artificial intelligence (AI) techniques in wireless infrastructure, real-time collection, and processing of end-user devices is now in high demand. It is now superlative to use AI to detect and predict pandemics of a colossal nature. The Coronavirus disease 2019 (COVID-19) pandemic, which originated in Wuhan China, has had disastrous effects on the global community and has overburdened advanced healthcare systems throughout the world. Globally; over 4,063,525 confirmed cases and 282,244 deaths have been recorded as of 11th May 2020, according to the European Centre for Disease Prevention and Control agency. However, the current rapid and exponential rise in the number of patients has necessitated efficient and quick prediction of the possible outcome of an infected patient for appropriate treatment using AI techniques. This paper proposes a fine-tuned Random Forest model boosted by the AdaBoost algorithm. The model uses the COVID-19 patient's geographical, travel, health, and demographic data to predict the severity of the case and the possible outcome, recovery, or death. The model has an accuracy of 94% and a F1 Score of 0.86 on the dataset used. The data analysis reveals a positive correlation between patients' gender and deaths, and also indicates that the majority of patients are aged between 20 and 70 years.
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              Multimodal deep learning models for early detection of Alzheimer’s disease stage

              Most current Alzheimer’s disease (AD) and mild cognitive disorders (MCI) studies use single data modality to make predictions such as AD stages. The fusion of multiple data modalities can provide a holistic view of AD staging analysis. Thus, we use deep learning (DL) to integrally analyze imaging (magnetic resonance imaging (MRI)), genetic (single nucleotide polymorphisms (SNPs)), and clinical test data to classify patients into AD, MCI, and controls (CN). We use stacked denoising auto-encoders to extract features from clinical and genetic data, and use 3D-convolutional neural networks (CNNs) for imaging data. We also develop a novel data interpretation method to identify top-performing features learned by the deep-models with clustering and perturbation analysis. Using Alzheimer’s disease neuroimaging initiative (ADNI) dataset, we demonstrate that deep models outperform shallow models, including support vector machines, decision trees, random forests, and k-nearest neighbors. In addition, we demonstrate that integrating multi-modality data outperforms single modality models in terms of accuracy, precision, recall, and meanF1 scores. Our models have identified hippocampus, amygdala brain areas, and the Rey Auditory Verbal Learning Test (RAVLT) as top distinguished features, which are consistent with the known AD literature.

                Author and article information

                Front Public Health
                Front Public Health
                Front. Public Health
                Frontiers in Public Health
                Frontiers Media S.A.
                07 February 2022
                : 10
                : 834032
                [1] 1Department of Electronics and Communication Engineering, Sethu Institute of Technology , Kariapatti, India
                [2] 2College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation , Doha, Qatar
                [3] 3Department of Information Technology, College of Computers and Information Technology, Taif University , Taif, Saudi Arabia
                [4] 4Information Systems Department, Faculty of Management, Comenius University in Bratislava , Bratislava, Slovakia
                [5] 5School of Creative Tech, University of Bolton , Bolton, United Kingdom
                Author notes

                Edited by: Thippa Reddy Gadekallu, VIT University, India

                Reviewed by: Rathinaraja Jeyaraj, Kyungpook National University, South Korea; Praveen Kumar, VIT University, India; Mukund Janardhanan, University of Leicester, United Kingdom

                *Correspondence: Ahila A akhilaamarnath27@ 123456gmail.com

                This article was submitted to Digital Public Health, a section of the journal Frontiers in Public Health

                Copyright © 2022 A, M, Hamdi, Bourouis, Rastislav and Mohmed.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                : 12 December 2021
                : 05 January 2022
                Page count
                Figures: 11, Tables: 3, Equations: 3, References: 26, Pages: 10, Words: 5487
                Public Health
                Original Research

                alzheimer's disease,accuracy,convolutional neural network,deep learning,feature extraction,image analysis,image classification and positron emission tomography


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