Machine learning technique for early detection of Alzheimer’s disease

Microsystem Technologies - Alzheimer’s disease (AD) is non-repairable brain disorder which impacts a person’s thinking along with shrinking the size of the brain, ultimately resulting...     

Integrating discretization and association rule-based classification for Alzheimer’s disease diagnosis

This paper shows a computer aided diagnosis (CAD) combining continuous attribute discretization and association rule mining for the early diagnosis of Alzheimer’s disease (AD) based on emission computed tomography images. A mask is obtained from the mean control images by an image histogram segmentation. 3D voxels centered in mask coordinates are selected by equal-width binning-based discretization of the mean intensity. These Regions of Interest (ROIs) are then used as input for the Association Rule (AR)-mining using control subject images to fully characterize the normal pattern of the image. Minimum support and confidence are fixed to the maximum values in order to obtain the highest predictive power rules for each discretization level (or combination of levels). Finally, classification is carried out by comparing the number of ARs verified by each subject under test. The proposed system is evaluated using two different databases of single photon emission computed tomography (SPECT) and positron emission tomography (PET) images from the Alzheimer Disease Neuroimaging Initiative (ADNI) yielding an accuracy up to 96.91% (for SPECT) and 92% (for PET), thus outperforming the baseline (the so called continuous AR-based method) and other recently reported CAD methods.     

Early diagnosis of Alzheimer’s disease based on Partial Least Squares and Support Vector Machine

An accurate and early diagnosis of the Alzheimer’s disease (AD) is of fundamental importance for the patient medical treatment. It has been shown that pathological manifestations of AD may be detected thought functional images even before that the patients becomes symptomatic. This fact has led researchers to propose new ways for analyzing functional data in order to get more accurate Computer-Aided Diagnosis (CAD) systems for this disorder. In this paper we show an effective approach for Single Photon Emission Computed Tomography feature extraction that improves the accuracy of CAD systems for AD. The proposed methodology uses a Partial Least Squares algorithm for extracting score vectors and the Out-Of-Bag error for selecting a number of scores that are used as features. Subsequently, a Support Vector Machine (SVM) based classifier determines the underlying class of the images, thus performing diagnostics. In order to test this approach we have used an image database for AD with 97 SPECT images from controls and AD patients. The images were visually labeled by experienced clinicians after the properly normalization. Several experiments have been developed to compare the proposed methodology and previous approaches. The results show that our method yields accuracy rates over 90%, outperforming several recently reported CAD systems for AD diagnosis.     

Association rule-based feature selection method for Alzheimer’s disease diagnosis

A fundamental challenge that remains unsolved in the neuroimage field is the small sample size problem. Feature selection and extraction, which are based on a limited training set, are likely to display poor generalization performance on new datasets. To address this challenge, a novel voxel selection method based on association rule (AR) mining is proposed for designing a computer aided diagnosis (CAD) system. The proposed method is tested as a tool for the early diagnosis of Alzheimer’s disease (AD). Discriminant brain areas are selected from a single photon emission computed tomography (SPECT) or positron emission tomography (PET) databases by means of an AR mining process. Simultaneously activated brain regions in control subjects that consist of the set of voxels defining the antecedents and consequents of the ARs are selected as input voxels for posterior dimensionality reduction. Feature extraction is defined by a subsequent reduction of the selected voxels using principal component analysis (PCA) or partial least squares (PLS) techniques while classification is performed by a support vector machine (SVM). The proposed method yields an accuracy up to 91.75% (with 89.29% sensitivity and 95.12% specificity) for SPECT and 90% (with 89.33% sensitivity and 90.67% specificity) for PET, thus improving recently developed methods for early diagnosis of AD.     

T-test based Alzheimer’s disease diagnosis with multi-feature in MRIs

Multimedia Tools and Applications - Diagnosing Alzheimer’s disease (AD) with magnetic resonance imaging (MRI) has attracted increasing attention. In this paper, we propose a new feature...     

Automatic classification and prediction models for early Parkinson’s disease diagnosis from SPECT imaging

Early and accurate diagnosis of Parkinson’s disease (PD) is important for early management, proper prognostication and for initiating neuroprotective therapies once they become available. Recent neuroimaging techniques such as dopaminergic imaging using single photon emission computed tomography (SPECT) with ¹²³I-Ioflupane (DaTSCAN) have shown to detect even early stages of the disease. In this paper, we use the striatal binding ratio (SBR) values that are calculated from the ¹²³I-Ioflupane SPECT scans (as obtained from the Parkinson’s progression markers initiative (PPMI) database) for developing automatic classification and prediction/prognostic models for early PD. We used support vector machine (SVM) and logistic regression in the model building process. We observe that the SVM classifier with RBF kernel produced a high accuracy of more than 96% in classifying subjects into early PD and healthy normal; and the logistic model for estimating the risk of PD also produced high degree of fitting with statistical significance indicating its usefulness in PD risk estimation. Hence, we infer that such models have the potential to aid the clinicians in the PD diagnostic process.     

Multi-view feature selection and classification for Alzheimer’s Disease diagnosis

Multimedia Tools and Applications - In our present society, Alzheimer’s disease (AD) is the most common dementia form in elderly people and has been a big social health problem worldwide. In...     

Insight into an unsupervised two-step sparse transfer learning algorithm for speech diagnosis of Parkinson’s disease

Neural Computing and Applications - Speech diagnosis of Parkinson’s disease (PD) as a non-invasive and simple diagnosis method is particularly worth exploring. However, the number of samples...     

Alzheimer’s disease diagnosis based on long-range dependency mechanism using convolutional neural network

Multimedia Tools and Applications - Being able to collect rich morphological information of brain, structural magnetic resonance imaging (MRI) is popularly applied to computer-aided diagnosis of...     

Deep-MEG: spatiotemporal CNN features and multiband ensemble classification for predicting the early signs of Alzheimer’s disease with magnetoencephalography

Neural Computing and Applications - In this paper, we present the novel Deep-MEG approach in which image-based representations of magnetoencephalography (MEG) data are combined with ensemble...     

Early detection of Alzheimer’s disease based on the state-of-the-art deep learning approach: a comprehensive survey

Multimedia Tools and Applications - Alzheimer’s disease (AD) is a form of brain disorder that causes functions’ loss in a person’s daily activity. Due to the tremendous progress...     

Outlier based literature exploration for cross-domain linking of Alzheimer’s disease and gut microbiota

In knowledge discovery, experts frequently need to combine knowledge from different domains to get new insights and derive new conclusions. Intelligent systems should support the experts in the search for relationships between concepts from different domains, where huge amounts of possible combinations require the systems to be efficient but also sufficiently general, open and interactive to enable the experts to creatively guide the discovery process. The paper proposes a cross-domain literature mining methodology that achieves this functionality by combining the functionality of two complementary text mining tools: clustering and topic ontology creation tool OntoGen and cross-domain bridging terms exploration tool CrossBee. Focusing on outlier documents identified by OntoGen contributes to the efficiency, while CrossBee allows for flexible and user-friendly bridging concepts exploration and identification. The proposed approach, which is domain independent and can support cross-domain knowledge discovery in any field of science, is illustrated on a biomedical case study dealing with Alzheimer’s disease, one of the most threatening age-related diseases, deteriorating lives of numerous individuals and challenging the ageing society as a whole. By applying the proposed methodology to Alzheimer’s disease and gut microbiota PubMed articles, we have identified Nitric oxide synthase (NOS) as a potentially valuable link between these two domains. The results support the hypothesis of neuroinflammatory nature of Alzheimer’s disease, and is indicative for the quest for identifying strategies to control nitric oxide-associated pathways in the periphery and in the brain. By addressing common mediators of inflammation using literature-based discovery, we have succeeded to uncover previously unidentified molecular links between Alzheimer’s disease and gut microbiota with a multi-target therapeutic potential.     

Early detection of Alzheimer’s disease using local binary pattern and convolutional neural network

Multimedia Tools and Applications - Alzheimer’s disease, a progressive and irreversible abnormality of the human brain impairs memory and thinking skills. Gradually, it will damage the...     

Analysis of vertical eye movements in Parkinson’s disease and its potential for diagnosis

Applied Intelligence - It is well known that eye movements are highly affected by Parkinson’s disease. The majority of studies related to effects of Parkinson’s disease on eye movements...     

Early diagnosis model of Alzheimer’s Disease based on sparse logistic regression

Multimedia Tools and Applications - Accurate classification of Alzheimer’s Disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI) are critical for the effective treatment...     

The use of decision tree induction and artificial neural networks for automatic diagnosis of Hashimoto’s disease

Methods for classification of ultrasound thyroid images have been presented. These methods allow us to classify examined patients as either sick or healthy. Decision tree induction and a multilayer perceptron neural network have been used to build classification models. Test results showed that the proposed methods can provide a starting point for building a support system in the process of medical diagnosis. Better accuracy of classifiers was achieved for the normalized images. We have also found that, under adopted assumptions, the results obtained for them were statistically significant in contrast to original images. The proposed methods allow us to separate a fairly large group of incorrectly classified cases. According to the authors, this group may contain features of the early stage of Hashimoto’s disease.     

Auxiliary diagnosis of heterogeneous data of Parkinson’s disease based on improved convolution neural network

Multimedia Tools and Applications - Parkinson’s disease (PD) is a kind of nervous system degenerative disease frequently occurring in the elderly over sixty years old. With the development of...     

Two approaches to selecting set of voxels for the diagnosis of Alzheimer?s disease using brain SPECT images

This paper presents a computer-aided diagnosis technique for improving the accuracy of the early diagnosis of the Alzheimer type dementia. The first proposed methodology is based on the selection of those voxels which present a greater difference between Controls and Alzheimer type dementia patients. The mean value of the intensities of the selected voxels is used as features for different classifiers. The proposed methodology reaches an accuracy of 89% in the classification task. A second criterion is chosen to select voxels based on considering those which present not only greater overall difference between both modalities (Controls and Alzheimer) but also present lower dispersion. The classification accuracy using this second condition increases to 94%.