Early Detection of Alzheimer's Disease Based on Clinical Trials, Three-Dimensional Imaging Data, and Personal Information Using Autoencoders.

BACKGROUND: A timely diagnosis of Alzheimer's disease (AD) is crucial to obtain more practical treatments. In this article, a novel approach using Auto-Encoder Neural Networks (AENN) for early detection of AD was proposed. METHOD: The proposed method mainly deals with the classification of multimodal data and the imputation of missing data. The data under study involve the MiniMental State Examination, magnetic resonance imaging, positron emission tomography, cerebrospinal fluid data, and personal information. Natural logarithm was used for normalizing the data. The Auto-Encoder Neural Networks was used for imputing missing data. Principal component analysis algorithm was used for reducing dimensionality of data. Support Vector Machine (SVM) was used as classifier. The proposed method was evaluated using Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Then, 10fold crossvalidation was used to audit the detection accuracy of the method. RESULTS: The effectiveness of the proposed approach was studied under several scenarios considering 705 cases of ADNI database. In three binary classification problems, that is AD vs. normal controls (NCs), mild cognitive impairment (MCI) vs. NC, and MCI vs. AD, we obtained the accuracies of 95.57%, 83.01%, and 78.67%, respectively. CONCLUSION: Experimental results revealed that the proposed method significantly outperformed most of the stateoftheart methods.

Emphasis Learning, Features Repetition in Width Instead of Length to Improve Classification Performance: Case Study-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.

Extracting, Recognizing, and Counting White Blood Cells from Microscopic Images by Using Complex-valued Neural Networks.

In this paper a method related to extracting white blood cells (WBCs) from blood microscopic images and recognizing them and counting each kind of WBCs is presented. In medical science diagnosis by check the number of WBCs and compared with normal number of them is a new challenge and in this context has been discussed it. After reviewing the methods of extracting WBCs from hematology images, because of high applicability of artificial neural networks (ANNs) in classification we decided to use this effective method to classify WBCs, and because of high speed and stable convergence of complex-valued neural networks (CVNNs) compare to the real one, we used them to classification purpose. In the method that will be introduced, first the white blood cells are extracted by RGB color system's help. In continuance, by using the features of each kind of globules and their color scheme, a normalized feature vector is extracted, and for classifying, it is sent to a complex-valued back-propagation neural network. And at last, the results are sent to the output in the shape of the quantity of each of white blood cells. Despite the low quality of the used images, our method has high accuracy in extracting and recognizing WBCs by CVNNs, and because of this, certainly its result on high quality images will be acceptable. Learning time of complex-valued neural networks, that are used here, was significantly less than real-valued neural networks.