scholarly journals Transfer Learning Assisted Classification and Detection of Alzheimer’s Disease Stages Using 3D MRI Scans

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2645 ◽  
Author(s):  
Muazzam Maqsood ◽  
Faria Nazir ◽  
Umair Khan ◽  
Farhan Aadil ◽  
Habibullah Jamal ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Transfer Learning ◽  
Brain Magnetic Resonance Imaging ◽  
Fine Tuning ◽  
Brain Cells ◽  
Daily Routine ◽  
Convolutional Network ◽  
Segmented Images ◽  
Multi Class Classification

Alzheimer’s disease effects human brain cells and results in dementia. The gradual deterioration of the brain cells results in disability of performing daily routine tasks. The treatment for this disease is still not mature enough. However, its early diagnosis may allow restraining the spread of disease. For early detection of Alzheimer’s through brain Magnetic Resonance Imaging (MRI), an automated detection and classification system needs to be developed that can detect and classify the subject having dementia. These systems also need not only to classify dementia patients but to also identify the four progressing stages of dementia. The proposed system works on an efficient technique of utilizing transfer learning to classify the images by fine-tuning a pre-trained convolutional network, AlexNet. The architecture is trained and tested over the pre-processed segmented (Grey Matter, White Matter, and Cerebral Spinal Fluid) and un-segmented images for both binary and multi-class classification. The performance of the proposed system is evaluated over Open Access Series of Imaging Studies (OASIS) dataset. The algorithm showed promising results by giving the best overall accuracy of 92.85% for multi-class classification of un-segmented images.

Deep Transfer Learning for Alzheimer's disease detection

2021 ◽  
Author(s):  
Nicole Dalia Cilia ◽  
Claudio De Stefano ◽  
Claudio Marrocco ◽  
Francesco Fontanella ◽  
Mario Molinara ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Transfer Learning ◽  
Disease Detection

A Transfer Learning Approach for Early Diagnosis of Alzheimer’s Disease on MRI Images

Neuroscience ◽  
2021 ◽  
Author(s):  
Atif Mehmood ◽  
Shuyuan yang ◽  
Zhixi feng ◽  
Min wang ◽  
AL Smadi Ahmad ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Early Diagnosis ◽  
Transfer Learning ◽  
Learning Approach

scholarly journals A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer’s disease

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shaker El-Sappagh ◽  
Jose M. Alonso ◽  
S. M. Riazul Islam ◽  
Ahmad M. Sultan ◽  
Kyung Sup Kwak
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cross Validation ◽  
Disease Risk ◽  
Binary Classification ◽  
Fuzzy Rule ◽  
Large Set ◽  
Detection Model ◽  
Multi Class Classification ◽  
Clinical Measures

AbstractAlzheimer’s disease (AD) is the most common type of dementia. Its diagnosis and progression detection have been intensively studied. Nevertheless, research studies often have little effect on clinical practice mainly due to the following reasons: (1) Most studies depend mainly on a single modality, especially neuroimaging; (2) diagnosis and progression detection are usually studied separately as two independent problems; and (3) current studies concentrate mainly on optimizing the performance of complex machine learning models, while disregarding their explainability. As a result, physicians struggle to interpret these models, and feel it is hard to trust them. In this paper, we carefully develop an accurate and interpretable AD diagnosis and progression detection model. This model provides physicians with accurate decisions along with a set of explanations for every decision. Specifically, the model integrates 11 modalities of 1048 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) real-world dataset: 294 cognitively normal, 254 stable mild cognitive impairment (MCI), 232 progressive MCI, and 268 AD. It is actually a two-layer model with random forest (RF) as classifier algorithm. In the first layer, the model carries out a multi-class classification for the early diagnosis of AD patients. In the second layer, the model applies binary classification to detect possible MCI-to-AD progression within three years from a baseline diagnosis. The performance of the model is optimized with key markers selected from a large set of biological and clinical measures. Regarding explainability, we provide, for each layer, global and instance-based explanations of the RF classifier by using the SHapley Additive exPlanations (SHAP) feature attribution framework. In addition, we implement 22 explainers based on decision trees and fuzzy rule-based systems to provide complementary justifications for every RF decision in each layer. Furthermore, these explanations are represented in natural language form to help physicians understand the predictions. The designed model achieves a cross-validation accuracy of 93.95% and an F1-score of 93.94% in the first layer, while it achieves a cross-validation accuracy of 87.08% and an F1-Score of 87.09% in the second layer. The resulting system is not only accurate, but also trustworthy, accountable, and medically applicable, thanks to the provided explanations which are broadly consistent with each other and with the AD medical literature. The proposed system can help to enhance the clinical understanding of AD diagnosis and progression processes by providing detailed insights into the effect of different modalities on the disease risk.

Alzheimer’s disease and its related Dementia types: A review on their management via nanotechnology based therapeutic strategies

2021 ◽  
Vol 18 ◽  
Author(s):  
Panoraia I. Siafaka ◽  
Gökce Mutlu ◽  
Neslihan Üstündağ Okur
Keyword(s):  
Alzheimer’S Disease ◽  
Drug Delivery ◽  
Alzheimer's Disease ◽  
Vascular Dementia ◽  
Drug Delivery Systems ◽  
Delivery Systems ◽  
The Body ◽  
Daily Routine ◽  
Mixed Dementia ◽  
The Brain

Background: Dementia and its related types such as Alzheimer’s disease, vascular dementia and mixed dementia belong to brain associated diseases, resulting in long-term progressive memory loss. These diseases are so severe that can affect a person's daily routine. Up to date, treatment of de- mentias is still an unmet challenge due to their complex pathophysiology and unavailable efficient pharmacological approaches. The use of nanotechnology based pharmaceutical products could possibly improve the management of dementia given that nanocarriers could more efficiently deliver drugs to the brain. Objective: The objective of this study is to provide the current nanotechnology based drug delivery systems for the treatment of various dementia types. In addition, the current diagnosis biomarkers for the mentioned dementia types along with their available pharmacological treatment are being dis- cussed. Method: An extensive review of the current nanosystems such as brain drug delivery systems against Alzheimer’s disease, vascular dementia and mixed dementia was performed. Moreover, nan- otheranostics as possible imaging markers for such dementias were also reported. Results: The field of nanotechnology is quite advantageous for targeting dementia given that nanoscale drug delivery systems easily penetrate the blood brain barrier and circulate in the body for prolonged time. These nanoformulations consist of polymeric nanoparticles, solid lipid nanoparticles, nanostruc- tured lipid carriers, microemulsions, nanoemulsions, and liquid crystals. The delivery of the nan- otherapeutics can be achieved via various administration routes such as transdermal, injectable, oral, and more importantly, through the intranasal route. Nonetheless, the nanocarriers are mostly limited to Alzheimer’s disease targeting; thus, nanocarriers for other types of dementia should be developed. Conclusion: To conclude, understanding the mechanism of neurodegeneration and reviewing the cur- rent drug delivery systems for Alzheimer’s disease and other dementia types are significant for medical and pharmaceutical society to produce efficient therapeutic choices and novel strategies based on mul- tifunctional and biocompatible nanocarriers, which can deliver the drug sufficiently into the brain.

Automatic Diagnosis of Alzheimer’s Disease Using Deep Learning Techniques

2021 ◽  
Vol 19 (11) ◽  
pp. 126-140
Author(s):  
Zahraa S. Aaraji ◽  
Hawraa H. Abbas
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Data Analysis ◽  
Binary Classification ◽  
Brain Mri ◽  
Three Dimensional ◽  
Structural Variations ◽  
Automatic Feature Extraction ◽  
Multi Class Classification

Neuroimaging data analysis has attracted a great deal of attention with respect to the accurate diagnosis of Alzheimer’s disease (AD). Magnetic Resonance Imaging (MRI) scanners have thus been commonly used to study AD-related brain structural variations, providing images that demonstrate both morphometric and anatomical changes in the human brain. Deep learning algorithms have already been effectively exploited in other medical image processing applications to identify features and recognise patterns for many diseases that affect the brain and other organs; this paper extends on this to describe a novel computer aided software pipeline for the classification and early diagnosis of AD. The proposed method uses two types of three-dimensional Convolutional Neural Networks (3D CNN) to facilitate brain MRI data analysis and automatic feature extraction and classification, so that pre-processing and post-processing are utilised to normalise the MRI data and facilitate pattern recognition. The experimental results show that the proposed approach achieves 97.5%, 82.5%, and 83.75% accuracy in terms of binary classification AD vs. cognitively normal (CN), CN vs. mild cognitive impairment (MCI) and MCI vs. AD, respectively, as well as 85% accuracy for multi class-classification, based on publicly available data sets from the Alzheimer’s disease Neuroimaging Initiative (ADNI).

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