scholarly journals A Deep Siamese Convolution Neural Network for Multi-Class Classification of Alzheimer Disease

2020 ◽  
Vol 10 (2) ◽  
pp. 84 ◽  
Author(s):  
Atif Mehmood ◽  
Muazzam Maqsood ◽  
Muzaffar Bashir ◽  
Yang Shuyuan
Keyword(s):  
Neural Network ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Convolutional Neural Network ◽  
State Of The Art ◽  
Early Stage ◽  
The State ◽  
Test Accuracy ◽  
Learning Approaches

Alzheimer’s disease (AD) may cause damage to the memory cells permanently, which results in the form of dementia. The diagnosis of Alzheimer’s disease at an early stage is a problematic task for researchers. For this, machine learning and deep convolutional neural network (CNN) based approaches are readily available to solve various problems related to brain image data analysis. In clinical research, magnetic resonance imaging (MRI) is used to diagnose AD. For accurate classification of dementia stages, we need highly discriminative features obtained from MRI images. Recently advanced deep CNN-based models successfully proved their accuracy. However, due to a smaller number of image samples available in the datasets, there exist problems of over-fitting hindering the performance of deep learning approaches. In this research, we developed a Siamese convolutional neural network (SCNN) model inspired by VGG-16 (also called Oxford Net) to classify dementia stages. In our approach, we extend the insufficient and imbalanced data by using augmentation approaches. Experiments are performed on a publicly available dataset open access series of imaging studies (OASIS), by using the proposed approach, an excellent test accuracy of 99.05% is achieved for the classification of dementia stages. We compared our model with the state-of-the-art models and discovered that the proposed model outperformed the state-of-the-art models in terms of performance, efficiency, and accuracy.

Classification of Alzheimer’s Disease via Eight-Layer Convolutional Neural Network with Batch Normalization and Dropout Techniques

2020 ◽  
Vol 10 (5) ◽  
pp. 1040-1048 ◽  
Author(s):  
Xianwei Jiang ◽  
Liang Chang ◽  
Yu-Dong Zhang
Keyword(s):  
Neural Network ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Convolutional Neural Network ◽  
Data Augmentation ◽  
State Of The Art ◽  
Training Dataset ◽  
Batch Normalization ◽  
Heavy Burden

More than 35 million patients are suffering from Alzheimer’s disease and this number is growing, which puts a heavy burden on countries around the world. Early detection is of benefit, in which the deep learning can aid AD identification effectively and gain ideal results. A novel eight-layer convolutional neural network with batch normalization and dropout techniques for classification of Alzheimer’s disease was proposed. After data augmentation, the training dataset contained 7399 AD patient and 7399 HC subjects. Our eight-layer CNN-BN-DO-DA method yielded a sensitivity of 97.77%, a specificity of 97.76%, a precision of 97.79%, an accuracy of 97.76%, a F1 of 97.76%, and a MCC of 95.56% on the test set, which achieved the best performance in seven state-of-the-art approaches. The results strongly demonstrate that this method can effectively assist the clinical diagnosis of Alzheimer’s disease.

scholarly journals Diagnosis and Classification of Alzheimer's Disease by Using a Convolution Neural Network Algorithm

2021 ◽  
Author(s):  
Mosleh Hmoud Al-Adhaileh
Keyword(s):  
Neural Network ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Early Stage ◽  
Brain Mri ◽  
Brain Magnetic Resonance Imaging ◽  
Classification Systems ◽  
Detection Accuracy ◽  
Learning Approaches

Abstract Alzheimer's disease (AD) is a high-risk and atrophic neurological illness that slowly and gradually destroys brain cells (i.e. neurons). As the most common type of amentia, AD affects 60–65% of all people with amentia and poses major health dangers to middle-aged and elderly people. For classification of AD in the early stage, classification systems and computer-aided diagnostic techniques have been developed. Previously, machine learning approaches were applied to develop diagnostic systems by extracting features from neural images. Currently, deep learning approaches have been used in many real-time medical imaging applications. In this study, two deep neural network techniques, AlexNet and Restnet50, were applied for the classification and recognition of AD. The data used in this study to evaluate and test the proposed model included those from brain magnetic resonance imaging (MRI) images collected from the Kaggle website. A convolutional neural network (CNN) algorithm was applied to classify AD efficiently. CNNs were pre-trained using AlexNet and Restnet50 transfer learning models. The results of this experimentation showed that the proposed method is superior to the existing systems in terms of detection accuracy. The AlexNet model achieved outstanding performance based on five evaluation metrics (accuracy, F1 score, precision, sensitivity and specificity) for the brain MRI datasets. AlexNet displayed an accuracy of 94.53%, specificity of 98.21%, F1 score of 94.12% and sensitivity of 100%, outperforming Restnet50. The proposed method can help improve CAD methods for AD in medical investigations.

scholarly journals Diagnosis of Alzheimer’s Disease Severity with fMRI Images Using Robust Multitask Feature Extraction Method and Convolutional Neural Network (CNN)

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Morteza Amini ◽  
MirMohsen Pedram ◽  
AliReza Moradi ◽  
Mahshad Ouchani
Keyword(s):  
Neural Network ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Feature Extraction ◽  
Convolutional Neural Network ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Early Stage ◽  
Support Vector ◽  
Feature Extraction Method

The automatic diagnosis of Alzheimer’s disease plays an important role in human health, especially in its early stage. Because it is a neurodegenerative condition, Alzheimer’s disease seems to have a long incubation period. Therefore, it is essential to analyze Alzheimer’s symptoms at different stages. In this paper, the classification is done with several methods of machine learning consisting of K -nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), linear discrimination analysis (LDA), and random forest (RF). Moreover, novel convolutional neural network (CNN) architecture is presented to diagnose Alzheimer’s severity. The relationship between Alzheimer’s patients’ functional magnetic resonance imaging (fMRI) images and their scores on the MMSE is investigated to achieve the aim. The feature extraction is performed based on the robust multitask feature learning algorithm. The severity is also calculated based on the Mini-Mental State Examination score, including low, mild, moderate, and severe categories. Results show that the accuracy of the KNN, SVM, DT, LDA, RF, and presented CNN method is 77.5%, 85.8%, 91.7%, 79.5%, 85.1%, and 96.7%, respectively. Moreover, for the presented CNN architecture, the sensitivity of low, mild, moderate, and severe status of Alzheimer patients is 98.1%, 95.2%,89.0%, and 87.5%, respectively. Based on the findings, the presented CNN architecture classifier outperforms other methods and can diagnose the severity and stages of Alzheimer’s disease with maximum accuracy.

Detection of Alzheimer’s Disease Using Deep Convolutional Neural Network

2021 ◽  
pp. 2140012
Author(s):  
Swapandeep Kaur ◽  
Sheifali Gupta ◽  
Swati Singh ◽  
Isha Gupta
Keyword(s):  
Neural Network ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Convolutional Neural Network ◽  
Early Stage ◽  
False Positive Rate ◽  
Classification Error ◽  
Classification Error Rate ◽  
Positive Rate ◽  
Inability To Work

Alzheimer’s disease (AD) is a disease that gradually develops and causes degeneration of the cells of the brain. The leading cause of AD is dementia that results in a person’s inability to work independently. In the early stages of AD, a person forgets recent conversations or the occurrence of an event. In the later stages, there could be severe loss of memory such that the person is not able to even perform everyday tasks. The medicines currently available for AD may improve its symptoms on a temporary basis in the early stage of the disease. Since no treatment is available for curing AD, its detection becomes extremely important. As the clinical treatments are very expensive, the need for automated diagnosis of AD is of critical importance. In this paper, a deep learning model based on a convolutional neural network has been used and applied to four classes of images of AD that is very mild demented, mild demented, average demented, and non-demented. It was found that the moderate demented class had the highest accuracy of 98.9%, a classification error rate of 0.01, and a specificity of 0.992. Also, the lowest false positive rate of 0.007 was obtained.

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