Alzheimer Disease Detection Techniques and Methods: A Review

Brain pathological changes linked with Alzheimer's disease (AD) can be measured with Neuroimaging. In the past few years, these measures are rapidly integrated into the signatures of Alzheimer disease (AD) with the help of classification frameworks which are offering tools for diagnosis and prognosis. Here is the review study of Alzheimer's disease based on Neuroimaging and cognitive impairment classification. This work is a systematic review for the published work in the field of AD especially the computer-aided diagnosis. The imaging modalities include 1) Magnetic resonance imaging (MRI) 2) Functional MRI (fMRI) 3) Diffusion tensor imaging 4) Positron emission tomography (PET) and 5) amyloid-PET. The study revealed that the classification criterion based on the features shows promising results to diagnose the disease and helps in clinical progression. The most widely used machine learning classifiers for AD diagnosis include Support Vector Machine, Bayesian Classifiers, Linear Discriminant Analysis, and K-Nearest Neighbor along with Deep learning. The study revealed that the deep learning techniques and support vector machine give higher accuracies in the identification of Alzheimer’s disease. The possible challenges along with future directions are also discussed in the paper

Predicting the course of Alzheimer's progression.

Alzheimer's disease is the most common neurodegenerative disease and is characterized by the accumulation of amyloid-beta peptides leading to the formation of plaques and tau protein tangles in brain. These neuropathological features precede cognitive impairment and Alzheimer's dementia by many years. To better understand and predict the course of disease from early-stage asymptomatic to late-stage dementia, it is critical to study the patterns of progression of multiple markers. In particular, we aim to predict the likely future course of progression for individuals given only a single observation of their markers. Improved individual-level prediction may lead to improved clinical care and clinical trials. We propose a two-stage approach to modeling and predicting measures of cognition, function, brain imaging, fluid biomarkers, and diagnosis of individuals using multiple domains simultaneously. In the first stage, joint (or multivariate) mixed-effects models are used to simultaneously model multiple markers over time. In the second stage, random forests are used to predict categorical diagnoses (cognitively normal, mild cognitive impairment, or dementia) from predictions of continuous markers based on the first-stage model. The combination of the two models allows one to leverage their key strengths in order to obtain improved accuracy. We characterize the predictive accuracy of this two-stage approach using data from the Alzheimer's Disease Neuroimaging Initiative. The two-stage approach using a single joint mixed-effects model for all continuous outcomes yields better diagnostic classification accuracy compared to using separate univariate mixed-effects models for each of the continuous outcomes. Overall prediction accuracy above 80% was achieved over a period of 2.5 years. The results further indicate that overall accuracy is improved when markers from multiple assessment domains, such as cognition, function, and brain imaging, are used in the prediction algorithm as compared to the use of markers from a single domain only

Fractal tool; Calculating 3D fractal dimension of brain regions for a Dementia classification problem

Η άνοια είναι ένα σύνδρομο που είναι κοινό στους ηλικιωμένους και ο ρυθμός εμφάνισής του αυξάνεται. Η πλειονότητα των μελετών επικεντρώνεται στην εύρεση βιοδεικτών για διάγνωση, ενώ η πρόληψη και η παρακολούθηση της ανάπτυξης είναι ακόμη ένα αδύνατο έργο. Σήμερα, η έρευνα για την άνοια περιορίζεται στη νευροαπεικόνιση, καθώς είναι μια μη επεμβατική τεχνολογία. Υπάρχει μια πληθώρα νευροαπεικονιστικών εργαλείων τα οποία βελτιστοποιούν την απεικόνιση μιας εισαγόμενης εικόνας μέσω της επεξεργασίας εικόνας ή ακόμη και συνεισφέρουν στη λήψη ιατρικών αποφάσεων μέσω της ανάλυσης της εικόνας. Ωστόσο, η ατροφία του εγκεφάλου στην άνοια δεν έχει ακόμη χαρακτηριστεί σωστά. Η νευροαπεικόνιση στοχεύει κυρίως στην παρακολούθηση της μείωσης του όγκου του εγκεφάλου και λιγότερο σε άλλες δομικές υφές. Τέτοια χαρακτηριστικά είναι η μορφοκλασματική διάσταση και η ύπαρξη κενών στις εγκαφαλικές δομές (lacunarity). Μερικά εργαλεία νευροαπεικόνισης υπολογίζουν τη διάσταση του φράκταλ και του lacunarity για ολόκληρο τον όγκο του εγκεφάλου. Ωστόσο, η λειτουργικότητά τους δεν περιλαμβάνει αυτοματοποιημένη εκτίμηση πολλαπλών εικόνων και, συνεπώς, δημιουργία συνόλων δεδομένων (datasets). Υπολογίζουν απλώς αυτά τα χαρακτηριστικά για συγκεκριμένες δομές, ενώ δεν εκτελούν τα απαραίτητα βήματα επεξεργασίας εικόνας. Δεδομένου ότι τα περισσότερα από αυτά τα εργαλεία εξειδικεύονται σε συγκεκριμένες εργασίες, δεν υπάρχει μια ολιστική μέθοδος που εισάγει πολλαπλά δεδομένα απεικόνισης και εξάγει μετρήσεις για τη μορφοκλασματική διάσταση και το lacunarity. Αυτή η μελέτη παρουσιάζει ένα εργαλείο γενικού σκοπού για την αυτοματοποιημένη επεξεργασία εικόνας, την τμηματοποίηση εικόνας, την εκτίμηση της μορφοκλασματική διάσταση, του lacunarity και άλλων υφών που προέρχονται από τον υπολογισμό της μορφοκλασματικής διάστασης. Τα εξαγόμενα αρχεία είναι σύνολα δεδομένων με τέτοιες μετρήσεις. Έπειτα γίνεται μια ταξινόμηση υγειών και ατόμων με άνοια η οποία επιβεβαιώνει τη χρησιμότητα του λογισμικού. Παρόλο που υπήρχαν περιορισμοί στην απόκτηση δεδομένων, πραγματοποιήθηκε αποτελεσματική ταξινόμηση με μηχανές διανυσματικής υποστήριξης. Για αρκετές περιοχές του εγκεφάλου, η ακρίβεια Fbeta score κυμαινόταν μεταξύ 95% και 100% υπερισχύοντας όλων των άλλων μεθόδων. Ωστόσο, η διάγνωση της άνοιας απαιτεί ένα μοντέλο που διαχωρίζει αποτελεσματικά τις περιοχές του εγκεφάλου για όλες τις κλάσεις. Σε αυτή τη διατριβή, εκπαιδεύτηκαν δύο τέτοια μοντέλα, ένα κάθε ομάδα. Παρ 'όλα αυτά, τα τελικά αποτελέσματα αναδεικνύουν την αναγκαιότητα των μορφοκλασματικών ιδιοτήτων ως εργαλείο για τη ταξινόμηση των σταδίων της άνοιας και τη παρακολούθηση της ανάπτυξης της άνοιας. Επίσης, η χρήση του λογισμικού μπορεί να επεκταθεί σε οποιοδήποτε πρόβλημα δομικής νευροαπεικόνισης όπως η ανίχνευση καρκίνουDementia is a syndrome that is common amongst the elder adults and its occurrence rate is on the rise. The majority of the studies are focused on finding biomarkers for diagnosis, while prevention and monitoring of the development is yet an impossible task. Nowadays, research on dementia is limited to neuroimaging as it is a non-invasive technology. There is a plethora of neuroimaging tools which optimize the virtualization of an imported image through image processing or even contribute medical decision making through image analysis. Still, brain atrophy in Dementia is yet to be characterized properly. Neuroimaging mainly aims on volume decline of brain volume and less on other structural textures. Such features are fractal dimension and lacunarity. Some neuroimaging tools calculate fractal dimension and lacunarity for whole brain volume. However, their functionality does not include automated estimation of multiple images and thus creation of datasets. They just compute these features for given structures while not performing the necessary image processing steps. As most of these tools are specialized in specific tasks, there is not a holistic method that inputs multiple imaging data and exports measurements for fractal dimension, lacunarity. This study presents a general purpose tool for automated image processing, image segmentation, estimation of fractal dimension, lacunarity and other textures that are derived from the calculation of fractal dimension. The exported files are datasets with such measurements. Finally, a classification between healthy and dementia subjects underlines the utility of the software. Even though there were limitations to data acquirement, efficient classification with SVM models has been performed. For several brain regions, the ranging Fbeta score accuracy was 97% - 100 % outperforming all other methods. However, diagnosis of Dementia requires a unique prior model which efficiently segment brain regions for any given class. In this thesis, two models were trained, one for each group. Nevertheless, the final results reveal the necessity of fractal properties as a tool for Dementia classification and monitoring of the Dementia development. Also, the utility of the software can be extended to any structural neuroimaging problem such as detection of cancer

Using Unsupervised Learning Methods to Analyse Magnetic Resonance Imaging (MRI) Scans for the Detection of Alzheimer’s Disease

Background: Alzheimer’s disease (AD) is the most common cause of dementia, characterised by behavioural and cognitive impairment. The manual diagnosis of AD by doctors is time-consuming and can be ineffective, so machine learning methods are increasingly being proposed to diagnose AD in many recent studies. Most research developing machine learning algorithms to diagnose AD use supervised learning to classify magnetic resonance imaging (MRI) scans. However, supervised learning requires a considerable volume of labelled data and MRI scans are difficult to label. The aim of this thesis was therefore to use unsupervised learning methods to differentiate between MRI scans from people who were cognitively normal (CN), people with mild cognitive impairment (MCI), and people with AD. Objectives: This study applied a statistical method and unsupervised learning methods to discriminate scans from (1) people with CN and with AD; (2) people with stable mild cognitive impairment (sMCI) and with progressive mild cognitive impairment (pMCI); (3) people with CN and with pMCI, using a limited number of labelled structural MRI scans. Methods: Two-sample t-tests were used to detect the regions of interest (ROIs) between each of the two groups (CN vs. AD; sMCI vs. pMCI; CN vs. pMCI), and then an unsupervised learning neural network was employed to extract features from the regions. Finally, a clustering algorithm was implemented to discriminate between each of the two groups based on the extracted features. The approach was tested on baseline brain structural MRI scans from 715 individuals from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), of which 231 were CN, 198 had AD, 152 had sMCI, and 134 were pMCI. The results were evaluated by calculating the overall accuracy, the sensitivity, specificity, and positive and negative predictive values. Results: The abnormal regions around the lower parts of the limbic system were indicated as AD-relevant regions based on the two-sample t-test (p<0.001), and the proposed method yielded an overall accuracy of 0.842 for discriminating between CN and AD, an overall accuracy of 0.672 for discriminating between sMCI and pMCI, and an overall accuracy of 0.776 for discriminating between CN and pMCI. Conclusion: The study combined statistical and unsupervised learning methods to identify scans of people with different stages of AD. This method can detect AD-relevant regions and could be used to accurately diagnose stages of AD; it has the advantage that it does not require large amounts of labelled MRI scans. The performances of the three discriminations were all comparable to those of previous state-of-the-art studies. The research in this thesis could be implemented in the future to help in the automatic diagnosis of AD and provide a basis for diagnosing sMCI and pMCI

Banknote Authentication and Medical Image Diagnosis Using Feature Descriptors and Deep Learning Methods

Banknote recognition and medical image analysis have been the foci of image processing and pattern recognition research. As counterfeiters have taken advantage of the innovation in print media technologies for reproducing fake monies, hence the need to design systems which can reassure and protect citizens of the authenticity of banknotes in circulation. Similarly, many physicians must interpret medical images. But image analysis by humans is susceptible to error due to wide variations across interpreters, lethargy, and human subjectivity. Computer-aided diagnosis is vital to improvements in medical analysis, as they facilitate the identification of findings that need treatment and assist the expert’s workflow. Thus, this thesis is organized around three such problems related to Banknote Authentication and Medical Image Diagnosis. In our first research problem, we proposed a new banknote recognition approach that classifies the principal components of extracted HOG features. We further experimented on computing HOG descriptors from cells created from image patch vertices of SURF points and designed a feature reduction approach based on a high correlation and low variance filter. In our second research problem, we developed a mobile app for banknote identification and counterfeit detection using the Unity 3D software and evaluated its performance based on a Cascaded Ensemble approach. The algorithm was then extended to a client-server architecture using SIFT and SURF features reduced by Bag of Words and high correlation-based HOG vectors. In our third research problem, experiments were conducted on a pre-trained mobile app for medical image diagnosis using three convolutional layers with an Ensemble Classifier comprising PCA and bagging of five base learners. Also, we implemented a Bidirectional Generative Adversarial Network to mitigate the effect of the Binary Cross Entropy loss based on a Deep Convolutional Generative Adversarial Network as the generator and encoder with Capsule Network as the discriminator while experimenting on images with random composition and translation inferences. Lastly, we proposed a variant of the Single Image Super-resolution for medical analysis by redesigning the Super Resolution Generative Adversarial Network to increase the Peak Signal to Noise Ratio during image reconstruction by incorporating a loss function based on the mean square error of pixel space and Super Resolution Convolutional Neural Network layers

Deep Learning Detected Nutrient Deficiency in Chili Plant

Chili is a staple commodity that also affects the Indonesian economy due to high market demand. Proven in June 2019, chili is a contributor to Indonesia's inflation of 0.20% from 0.55%. One factor is crop failure due to malnutrition. In this study, the aim is to explore Deep Learning Technology in agriculture to help farmers be able to diagnose their plants, so that their plants are not malnourished. Using the RCNN algorithm as the architecture of this system. Use 270 datasets in 4 categories. The dataset used is primary data with chili samples in Boyolali Regency, Indonesia. The chili we use are curly chili. The results of this study are computers that can recognize nutrient deficiencies in chili plants based on image input received with the greatest testing accuracy of 82.61% and has the best mAP value of 15.57%

Radial Basis Function Neural Network in Identifying The Types of Mangoes

Mango (Mangifera Indica L) is part of a fruit plant species that have different color and texture characteristics to indicate its type. The identification of the types of mangoes uses the manual method through direct visual observation of mangoes to be classified. At the same time, the more subjective way humans work causes differences in their determination. Therefore in the use of information technology, it is possible to classify mangoes based on their texture using a computerized system. In its completion, the acquisition process is using the camera as an image processing instrument of the recorded images. To determine the pattern of mango data taken from several samples of texture features using Gabor filters from various types of mangoes and the value of the feature extraction results through artificial neural networks (ANN). Using the Radial Base Function method, which produces weight values, is then used as a process for classifying types of mangoes. The accuracy of the test results obtained from the use of extraction methods and existing learning methods is 100%