scholarly journals Computer-Aided Diagnosis of Alzheimer’s Disease via Deep Learning Models and Radiomics Method

2021 ◽  
Vol 11 (17) ◽  
pp. 8104
Author(s):  
Yin Dai ◽  
Wenhe Bai ◽  
Zheng Tang ◽  
Zian Xu ◽  
Weibing Chen
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Network Models ◽  
Disease Diagnosis ◽  
Classification Model ◽  
Neural Network Models ◽  
Fusion Images ◽  
Alzheimer’S Disease Diagnosis ◽  
Single Modality

This paper focused on the problem of diagnosis of Alzheimer’s disease via the combination of deep learning and radiomics methods. We proposed a classification model for Alzheimer’s disease diagnosis based on improved convolution neural network models and image fusion method and compared it with existing network models. We collected 182 patients in the ADNI and PPMI database to classify Alzheimer’s disease, and reached 0.906 AUC in training with single modality images, and 0.941 AUC in training with fusion images. This proved the proposed method has better performance in the fusion images. The research may promote the application of multimodal images in the diagnosis of Alzheimer’s disease. Fusion images dataset based on multi-modality images has higher diagnosis accuracy than single modality images dataset. Deep learning methods and radiomics significantly improve the diagnosing accuracy of Alzheimer’s disease diagnosis.

scholarly journals Emphasis Learning, Features Repetition in Width Instead of Length to Improve Classification Performance: Case Study—Alzheimer’s Disease Diagnosis

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 941
Author(s):  
Hamid Akramifard ◽  
MohammadAli Balafar ◽  
SeyedNaser Razavi ◽  
Abd Rahman Ramli
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Principal Component ◽  
Classification Problem ◽  
Disease Diagnosis ◽  
Classification Model ◽  
Support Vector ◽  
Svm Classifier ◽  
Small Subset ◽  
Alzheimer’S Disease Diagnosis

In the past decade, many studies have been conducted to advance computer-aided systems for Alzheimer’s disease (AD) diagnosis. Most of them have recently developed systems concentrated on extracting and combining features from MRI, PET, and CSF. For the most part, they have obtained very high performance. However, improving the performance of a classification problem is complicated, specifically when the model’s accuracy or other performance measurements are higher than 90%. In this study, a novel methodology is proposed to address this problem, specifically in Alzheimer’s disease diagnosis classification. This methodology is the first of its kind in the literature, based on the notion of replication on the feature space instead of the traditional sample space. Briefly, the main steps of the proposed method include extracting, embedding, and exploring the best subset of features. For feature extraction, we adopt VBM-SPM; for embedding features, a concatenation strategy is used on the features to ultimately create one feature vector for each subject. Principal component analysis is applied to extract new features, forming a low-dimensional compact space. A novel process is applied by replicating selected components, assessing the classification model, and repeating the replication until performance divergence or convergence. The proposed method aims to explore most significant features and highest-preforming model at the same time, to classify normal subjects from AD and mild cognitive impairment (MCI) patients. In each epoch, a small subset of candidate features is assessed by support vector machine (SVM) classifier. This repeating procedure is continued until the highest performance is achieved. Experimental results reveal the highest performance reported in the literature for this specific classification problem. We obtained a model with accuracies of 98.81%, 81.61%, and 81.40% for AD vs. normal control (NC), MCI vs. NC, and AD vs. MCI classification, respectively.

scholarly journals Deep Learning Framework for Alzheimer’s Disease Diagnosis via 3D-CNN and FSBi-LSTM

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 63605-63618 ◽  
Author(s):  
Chiyu Feng ◽  
Ahmed Elazab ◽  
Peng Yang ◽  
Tianfu Wang ◽  
Feng Zhou ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Disease Diagnosis ◽  
Learning Framework ◽  
3D Cnn ◽  
Alzheimer’S Disease Diagnosis

scholarly journals Automatic assessment of Alzheimer’s disease diagnosis based on deep learning techniques

2020 ◽  
Vol 120 ◽  
pp. 103764
Author(s):  
Alejandro Puente-Castro ◽  
Enrique Fernandez-Blanco ◽  
Alejandro Pazos ◽  
Cristian R. Munteanu
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Disease Diagnosis ◽  
Automatic Assessment ◽  
Learning Techniques ◽  
Alzheimer’S Disease Diagnosis
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