Recent Applications of Deep Learning Algorithms in Medical Image Analysis

Advances in deep learning have enabled researchers in the field of medical imaging to employ such techniques for various applications, including early diagnosis of different diseases. Deep learning techniques such as convolutional neural networks offer the capability of extracting invariant features from images that can improve the performance of most predictive models in medical and diagnostic imaging. This work concentrates on reviewing deep learning architectures along with medical imaging modalities where the crucial applications of such algorithms, including image classification and segmentation, are discussed. Also, brain imaging as a branch of medical imaging which allows scientists to explore the structure and function of the brain is explored, and the applications of deep learning to early diagnose Alzheimer’s Disease, and Autism as the most critical brain disorders are studied. Moreover, the recent research findings revealed that employing deep learning-based semantic segmentation techniques could significantly improve the accuracy of models developed for brain tumor detection. Such advances in early diagnosis of disorders and tumors encourage medical imaging practitioners to implement software applications assisting them to improve their decision-making process

Cognition and Cognitive Dynamic Systems Concepts and Applications in Project Risk Management

Cognition is the brain’s ability to perform high-level functions including understanding and information processing. In general, cognition is considered analytical rather than emotional. Cognitive models are mathematical representations of issues that are not necessarily mathematical. On the other hand, risk analysis and management is a process of defining factors or parameters that are mildly to severely dangerous for a given system involving businesses or individuals. Recently, research and investigations have explored the potential of utilizing cognitive models in risk management and analysis in order to better clarify risk factors in a system and control risk resources. In this paper, fundamentals of risk management and cognitive dynamic systems are discussed, and applications and various implementations of cognitive systems based on real-life examples are introduced

Binary Image Segmentation Using Classification Methods: Support Vector Machines, Artificial Neural Networks and Kth Nearest Neighbours

The principal objective of this work is to demonstrate efficient parameter selection for various networks used in binary image segmentation. The Support Vector Machines using four kernel functions (i.e., Radial Basis Function, Quadratic, Polynomial, and Linear), Neural Networks (i.e., Feed-forward Back-propagation) and Kth Nearest Neighbours algorithm were applied to five different datasets that had been generated from a given image. Pixel coordinates (x,y) were considered as inputs. Grid search and cross-validation were performed to identify the optimal network parameters. All experiments were repeated five times in order to develop confidence in the obtained results. High accuracy was achieved in most cases 95% for SVM-RBF, 90.4% for SVM-Quadratic, 90.8% for SVM-Polynomial, 60% for SVM-Linear, 88% for Neural Networks and 97% for K-NN. After grid search for SVM-RBF, the accuracy reached 98%. In this project, SVM-RBF showed a high level of accuracy and consistency. It was also found that the selected features (pixel coordinates) were discriminative

Template Matching Advances and Applications in Image Analysis

In most computer vision and image analysis problems, it is necessary to define a similarity measure between two or more different objects or images. Template matching is a classic and fundamental method used to score similarities between objects using certain mathematical algorithms. In this paper, we reviewed the basic concept of matching, as well as advances in template matching and applications such as invariant features or novel applications in medical image analysis. Additionally, deformable models and templates originating from classic template matching were discussed. These models have broad applications in image registration, and they are a fundamental aspect of novel machine vision or deep learning algorithms, such as convolutional neural networks (CNN), which perform shift and scale invariant functions followed by classification. In general, although template matching methods have restrictions which limit their application, they are recommended for use with other object recognition methods as pre- or post-processing steps. Combining a template matching technique such as normalized cross-correlation or dice coefficient with a robust decision-making algorithm yields a significant improvement in the accuracy rate for object detection and recognition

Template Matching Advances and Applications in Image Analysis

In most computer vision and image analysis problems, it is necessary to define a similarity measure between two or more different objects or images. Template matching is a classic and fundamental method used to score similarities between objects using certain mathematical algorithms. In this paper, we reviewed the basic concept of matching, as well as advances in template matching and applications such as invariant features or novel applications in medical image analysis. Additionally, deformable models and templates originating from classic template matching were discussed. These models have broad applications in image registration, and they are a fundamental aspect of novel machine vision or deep learning algorithms, such as convolutional neural networks (CNN), which perform shift and scale invariant functions followed by classification. In general, although template matching methods have restrictions which limit their application, they are recommended for use with other object recognition methods as pre- or post-processing steps. Combining a template matching technique such as normalized cross-correlation or dice coefficient with a robust decision-making algorithm yields a significant improvement in the accuracy rate for object detection and recognition

A Comprehensive Review of Deep Learning Architectures for Computer Vision Applications

The emergence of machine learning in the artificial intelligence field led the world of technology to make great strides. Today’s advanced systems with the ability of being designed just like human brain functions has given practitioners the ability to train systems so that they could process, analyze, classify, and predict different data classes. Therefore, the machine learning field has become a hot topic for scientists and researchers to introduce the best network with the highest performance for such mentioned purposes. In this article, computer vision science, image classification implementation, and deep neural networks are presented. This article discusses how models have been designed based on the concept of the human brain. The development of a Convolutional Neural Network (CNN) and its various architectures, which have shown great efficiency and evaluation in object detection, face recognition, image classification, and localization, are also introduced. Furthermore, the utilization and application of CNNs, including voice recognition, image processing, video processing, and text recognition, are examined closely. A literature review is conducted to illustrate the significance and the details of Convolutional Neural Networks in various applications

Effet de l’entraînement des fonctions exécutives sur l’utilisation appropriée de stratégies de mémoire au cours du vieillissement : étude comportementale et électrophysiologique

réalisé en co-tutelle avec l'Université François-Rabelais de ToursL’objectif général de cette thèse était de tester l’efficacité d’un entraînement exécutif ainsi que de tester les transferts des bénéfices de cet entraînement sur d’autres fonctions cognitives notamment la mémoire épisodique au niveau comportemental et électrophysiologique chez des adultes âgés. Pour cela, des effets test-retest ont été observés lors de la passation d’un même test de façon consécutive. Ainsi, la création de nouveaux tests pour confectionner le programme d’entraînement a été nécessaire pour ne pas avoir ces effets tests re-tests lors de la passation des pré-tests et des post-tests. Les entraînements exécutifs ont permis l’amélioration du fonctionnement exécutif mais aussi l’amélioration de la vitesse de traitement, et ces bénéfices ont permis l’annulation des effets d’âge dans le groupe entraîné sur une fonction exécutive : la mise à jour de la mémoire de travail. De plus, des effets de transfert ont été obtenus sur l’utilisation stratégique lors de l’encodage d’information en mémoire épisodique. Plus précisément, les adultes âgés entraînés utilisaient plus de stratégies d’encodages efficaces (encodage profond de type imagerie mentale ou phrase) après l’entraînement exécutif et l’utilisation de ces stratégies était plus efficace. Au niveau cérébral, l’entraînement exécutif semble engendrer des processus de spécialisation cérébrale se traduisant par une diminution de l’activité cérébrale de certaines zones cérébrales. Ce travail de thèse incite le développement de programmes d’entraînement des fonctions exécutives qui semblent permettre des effets de transfert à des tâches non entraînées et ces bénéfices semblent aussi modifier le fonctionnement cérébral, ce qui suppose un effet plus durable. Ceci confirme l’idée qu’un environnement stimulant cognitivement est en lien avec de bonnes capacités cognitives et contribue à un vieillissement réussi.The main objective of this thesis was to test the efficiency of an executive training program and the transfer effects of this program on other cognitive functions, in particular episodic memory strategies and performance. The present work was based on behavioral and electrophysiological data. Practice effects of executive tests have been first tested in young and older adults. Two tests have been practiced across ten practice sessions, and the results showed that the executive functions scores increased after practice, more for the older adults than for the younger ones. In order to develop an executive training program requiring several different tests, we have created new executive tests. The psychometric validity of these tests has been verified and confirmed. These tests have been used for our executive program. Thus, if training effects appeared following this program, they will not be due to the practice of a unique test. Eight sessions of executive stimulation with our new tests have allowed older adults increasing their executive functioning, measured by tests which were different from those employed in the training program (near transfer effects). Other cognitive functions were also improved, as processing speed and episodic memory. For the first time in the literature, far transfer effect have been found memory strategy efficiency and memory performance. More especially, the older adults trained group used more efficient memory strategies (mental imagery or making sentence with the words to be learnt) after the executive training program, and these strategies were more efficient to recall the words. Thus, the memory performance was increased in this group, in comparison the older adults group who did not participate to the training program. At a cerebral level, the executive training seems to decrease the duration of the cerebral activity for the same memory task. However, these results must to be taken with caution and require further analyses to be interpreted correctly. The results of this thesis encourages the development of training programs for executive functions which allows the transfer effects to untrained tasks. These benefits also appear to modify the brain functioning, which implies a longer lasting benefit effect. This work supports the idea that a cognitively stimulating environment is in line with higher cognitive abilities and contributes to successful agin