scholarly journals Personalized Computer-Aided Diagnosis for Mild Cognitive Impairment in Alzheimer’s Disease Based on sMRI and ¹¹C PiB-PET Analysis

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 218982-218996
Author(s):  
Fatma El-Zahraa A. El-Gamal ◽  
Mohammed M. Elmogy ◽  
Ashraf Khalil ◽  
Mohammed Ghazal ◽  
Jawad Yousaf ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Computer Aided Diagnosis ◽  
Computer Aided ◽  
Aided Diagnosis

Computer aided diagnosis of Alzheimer’s disease using component based SVM

2011 ◽  
Vol 11 (2) ◽  
pp. 2376-2382 ◽  
Author(s):  
I.A. Illán ◽  
J.M. Górriz ◽  
M.M. López ◽  
J. Ramírez ◽  
D. Salas-Gonzalez ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Computer Aided Diagnosis ◽  
Computer Aided ◽  
Aided Diagnosis

Research on Computer-Aided Diagnosis of Alzheimer’s Disease Based on Heterogeneous Medical Data Fusion

2019 ◽  
Vol 33 (05) ◽  
pp. 1957001
Author(s):  
Yin Dai ◽  
Daoyun Qiu ◽  
Yang Wang ◽  
Sizhe Dong ◽  
Hong-Li Wang
Keyword(s):  
Neural Network ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Convolutional Neural Network ◽  
Effective Means ◽  
Computer Aided Diagnosis ◽  
Test Results ◽  
Back Propagation Algorithm ◽  
Computer Aided ◽  
Aided Diagnosis

Alzheimer’s disease is the third most expensive disease, only after cancer and cardiopathy. It is also the fourth leading cause of death in the elderly after cardiopathy, cancer, and cerebral palsy. The disease lacks specific diagnostic criteria. At present, there is still no definitive and effective means for preclinical diagnosis and treatment. It is the only disease that cannot be prevented and cured among the world’s top ten fatal diseases. It has now been proposed as a global issue. Computer-aided diagnosis of Alzheimer’s disease (AD) is mostly based on images at this stage. This project uses multi-modality imaging MRI/PET combining with clinical scales and uses deep learning-based computer-aided diagnosis to treat AD, improves the comprehensiveness and accuracy of diagnosis. The project uses Bayesian model and convolutional neural network to train experimental data. The experiment uses the improved existing network model, LeNet-5, to design and build a 10-layer convolutional neural network. The network uses a back-propagation algorithm based on a gradient descent strategy to achieve good diagnostic results. Through the calculation of sensitivity, specificity and accuracy, the test results were evaluated, good test results were obtained.

scholarly journals A Personalized Computer-Aided Diagnosis System for Mild Cognitive Impairment (MCI) Using Structural MRI (sMRI)

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5416
Author(s):  
Fatma El-Zahraa A. El-Gamal ◽  
Mohammed Elmogy ◽  
Ali Mahmoud ◽  
Ahmed Shalaby ◽  
Andrew E. Switala ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Neurodegenerative Disorder ◽  
Early Stage ◽  
Brain Regions ◽  
Computer Aided Diagnosis ◽  
Cortical Region ◽  
Diagnosis System ◽  
Computer Aided ◽  
Aided Diagnosis

Alzheimer’s disease (AD) is a neurodegenerative disorder that targets the central nervous system (CNS). Statistics show that more than five million people in America face this disease. Several factors hinder diagnosis at an early stage, in particular, the divergence of 10–15 years between the onset of the underlying neuropathological changes and patients becoming symptomatic. This study surveyed patients with mild cognitive impairment (MCI), who were at risk of conversion to AD, with a local/regional-based computer-aided diagnosis system. The described system allowed for visualization of the disorder’s effect on cerebral cortical regions individually. The CAD system consists of four steps: (1) preprocess the scans and extract the cortex, (2) reconstruct the cortex and extract shape-based features, (3) fuse the extracted features, and (4) perform two levels of diagnosis: cortical region-based followed by global. The experimental results showed an encouraging performance of the proposed system when compared with related work, with a maximum accuracy of 86.30%, specificity 88.33%, and sensitivity 84.88%. Behavioral and cognitive correlations identified brain regions involved in language, executive function/cognition, and memory in MCI subjects, which regions are also involved in the neuropathology of AD.

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