Computer-aided Diagnosis Of Alzheimer Research Articles

Computer-aided diagnosis of Alzheimer's disease based on structural magnetic resonance imaging.

Computer-aided diagnosis of Alzheimer's disease based on structural magnetic resonance imaging.

A robust class decomposition-based approach for detecting Alzheimer's progression.

Computer-aided diagnosis of Alzheimer's disease (AD) is a rapidly growing field with the possibility to be utilized in practice. Deep learning has received much attention in detecting AD from structural magnetic resonance imaging (sMRI). However, training a convolutional neural network from scratch is problematic because it requires a lot of annotated data and additional computational time. Transfer learning can offer a promising and practical solution by transferring information learned from other image recognition tasks to medical image classification. Another issue is the dataset distribution's irregularities. A common classification issue in datasets is a class imbalance, where the distribution of samples among the classes is biased. For example, a dataset may contain more instances of some classes than others. Class imbalance is challenging because most machine learning algorithms assume that each class should have an equal number of samples. Models consequently perform poorly in prediction. Class decomposition can address this problem by making learning a dataset's class boundaries easier. Motivated by these approaches, we propose a class decomposition transfer learning (CDTL) approach that employs VGG19, AlexNet, and an entropy-based technique to detect AD from sMRI. This study aims to assess the robustness of the CDTL approach in detecting the cognitive decline of AD using data from various ADNI cohorts to determine whether comparable classification accuracy for the two or more cohorts would be obtained. Furthermore, the proposed model achieved state-of-the-art performance in predicting mild cognitive impairment (MCI)-to-AD conversion with an accuracy of 91.45%.

Monte Carlo Ensemble Neural Network for the diagnosis of Alzheimer’s disease

Convolutional neural networks (CNNs) have been increasingly used in the computer-aided diagnosis of Alzheimer’s Disease (AD). This study takes the advantage of the 2D-slice CNN fast computation and ensemble approaches to develop a Monte Carlo Ensemble Neural Network (MCENN) by introducing Monte Carlo sampling and an ensemble neural network in the integration with ResNet50. Our goals are to improve the 2D-slice CNN performance and to design the MCENN model insensitive to image resolution. Unlike traditional ensemble approaches with multiple base learners, our MCENN model incorporates one neural network learner and generates a large number of possible classification decisions via Monte Carlo sampling of feature importance within the combined slices. This can overcome the main weakness of the lack of 3D brain anatomical information in 2D-slice CNNs and develop a neural network to learn the 3D relevance of the features across multiple slices. Brain images from Alzheimer’s Disease Neuroimaging Initiative (ADNI, 7199 scans), the Open Access Series of Imaging Studies-3 (OASIS-3, 1992 scans), and a clinical sample (239 scans) are used to evaluate the performance of the MCENN model for the classification of cognitively normal (CN), patients with mild cognitive impairment (MCI) and AD. Our MCENN with a small number of slices and minimal image processing (rigid transformation, intensity normalization, skull stripping) achieves the AD classification accuracy of 90%, better than existing 2D-slice CNNs (accuracy: 63%∼84%) and 3D CNNs (accuracy: 74%∼88%). Furthermore, the MCENN is robust to be trained in the ADNI dataset and applied to the OASIS-3 dataset and the clinical sample. Our experiments show that the AD classification accuracy of the MCENN model is comparable when using high- and low-resolution brain images, suggesting the insensitivity of the MCENN to image resolution. Hence, the MCENN does not require high-resolution 3D brain structural images and comprehensive image processing, which supports its potential use in a clinical setting.

Computer-aided diagnosis of Alzheimer’s disease by MRI analysis and evolutionary computing

Computer-aided diagnosis of Alzheimer’s disease by MRI analysis and evolutionary computing

Computer-Aided Diagnosis of Alzheimer's Disease through Weak Supervision Deep Learning Framework with Attention Mechanism.

Alzheimer’s disease (AD) is the most prevalent neurodegenerative disease causing dementia and poses significant health risks to middle-aged and elderly people. Brain magnetic resonance imaging (MRI) is the most widely used diagnostic method for AD. However, it is challenging to collect sufficient brain imaging data with high-quality annotations. Weakly supervised learning (WSL) is a machine learning technique aimed at learning effective feature representation from limited or low-quality annotations. In this paper, we propose a WSL-based deep learning (DL) framework (ADGNET) consisting of a backbone network with an attention mechanism and a task network for simultaneous image classification and image reconstruction to identify and classify AD using limited annotations. The ADGNET achieves excellent performance based on six evaluation metrics (Kappa, sensitivity, specificity, precision, accuracy, F1-score) on two brain MRI datasets (2D MRI and 3D MRI data) using fine-tuning with only 20% of the labels from both datasets. The ADGNET has an F1-score of 99.61% and sensitivity is 99.69%, outperforming two state-of-the-art models (ResNext WSL and SimCLR). The proposed method represents a potential WSL-based computer-aided diagnosis method for AD in clinical practice.

Anatomical Landmarks and DAG Network Learning for Alzheimer’s Disease Diagnosis

The accurate diagnosis and prediction for individuals is crucial in computer-aided diagnosis of Alzheimer's disease (AD). The existing structural magnetic resonance imaging based classification methods of AD diagnosis mainly focus on the voxel level, region level and patch level morphological pattern analysis. However, most of these methods extract features with high dimension which may lead to overfitting problem. Besides, the interaction of different patches is not considered in the classifier ensemble. In this article, we propose a novel anatomical landmarks and directed acyclic graph (DAG) network feature learning based classification algorithm for the diagnosis of AD individuals. First, the anatomical feature patches of gray matter image are identified by the morphological and statistical analysis. Second, a simple and efficient DAG convolutional neural network is proposed to extract the discriminative deep features of image representation. Especially, the deep features are obtained by fusing feature maps of different network levels which contain semantic high-level and high-resolution low-level features. Finally, support vector machine and deep features are utilized to construct the classification model and predict the individual of AD. Experiments on three public datasets including ADNI-1, ADNI-2 and MIRIAD demonstrate that the proposed method can effectively improve the classification performance compared with the state-of-the-art methods for AD diagnosis.

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

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.

Methods for Аlzheimer’s desiease diagnostics

The problem of Alzheimer disease diagnosis is considered. The review of current existing automated methods of Alzheimer disease diagnosis using MRI and PET/SPECT images is given. Advantages and disadvantages are presented. Problem of potential redundancy of Alzheimer disease features, which are used in modern diagnosis systems, is considered. A feature selection algorithm was developed using statistical tests. The new approach based on a fuzzy logic application for the computer-aided diagnosis of Alzheimer’s disease is developed and experimentally investigated. References 34, figures 7, tables 2.

Complex wavelet algorithm for computer‐aided diagnosis of Alzheimer's disease

Electroencephalography signals are used for computer-aided diagnosis of Alzheimer's disease. Therefore, extracting critical features that belong to Alzheimer's signals are useful and tedious for neural network classification due to the high-frequency non-stationary components. For this purpose, time-frequency analysis and the multi-resolution capability of wavelets represent an attractive choice. However, fluctuations of the transformed coefficients and the absence of phase information make the process less accurate in certain scenarios. Because of this, complex wavelet transform has been selected to handle Alzheimer's signals. Moreover, the importance of calculating an optimal threshold value has been highlighted, usually by means of Shannon entropy as a helpful threshold identifier of the complex wavelet transform used to produce significant results. The effectiveness of Tsallis entropy instead of Shannon entropy in handling Alzheimer's signals is evaluated, the former giving place to better features for neural network classification. As a result, accuracy has been improved from 90 to 95% using Tsallis entropy. Hence, this new proposal boosts the opportunity to reduce mortality rates by detecting the disease accurately.

Computer-aided diagnosis of Alzheimer's disease using support vector machines and classification trees

This paper presents a computer-aided diagnosis technique for improving the accuracy of early diagnosis of Alzheimer-type dementia. The proposed methodology is based on the selection of voxels which present Welch's t-test between both classes, normal and Alzheimer images, greater than a given threshold. The mean and standard deviation of intensity values are calculated for selected voxels. They are chosen as feature vectors for two different classifiers: support vector machines with linear kernel and classification trees. The proposed methodology reaches greater than 95% accuracy in the classification task.

Computer-aided diagnosis of Alzheimer’s type dementia combining support vector machines and discriminant set of features

Alzheimer’s disease (AD) is the most common cause of dementia in the elderly and affects approximately 30 million individuals worldwide. With the growth of the older population in developed nations, the prevalence of AD is expected to triple over the next 50 years while its early diagnosis remains being a difficult task. Functional imaging modalities including Single Photon Emission Computed Tomography (SPECT) and positron emission tomography (PET) are often used with the aim of achieving early diagnosis. However, conventional evaluation of SPECT images often relies on manual reorientation, visual reading of tomographic slices and semiquantitative analysis of certain regions of interest (ROIs). These steps are time consuming, subjective and prone to error. This paper shows a fully automatic computer-aided diagnosis (CAD) system for improving the early detection of the AD. The proposed approach is based on image parameter selection and support vector machine (SVM) classification. A study is carried out in order to finding the ROIs and the most discriminant image parameters with the aim of reducing the dimensionality of the input space and improving the accuracy of the system. Among all the features evaluated, coronal standard deviation and sagittal correlation parameters are found to be the most effective ones for reducing the dimensionality of the input space and improving the diagnosis accuracy when a radial basis function (RBF) SVM is used. The proposed system yields a 90.38% accuracy in the early diagnosis of the AD and outperforms existing techniques including the voxel-as-features (VAF) approach.