Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates

The study of cerebral anatomy in developing neonates is of great importance for the understanding of brain development during the early period of life. This dissertation therefore focuses on three challenges in the modelling of cerebral anatomy in neonates during brain development. The methods that have been developed all use Magnetic Resonance Images (MRI) as source data. To facilitate study of vascular development in the neonatal period, a set of image analysis algorithms are developed to automatically extract and model cerebral vessel trees. The whole process consists of cerebral vessel tracking from automatically placed seed points, vessel tree generation, and vasculature registration and matching. These algorithms have been tested on clinical Time-of- Flight (TOF) MR angiographic datasets. To facilitate study of the neonatal cortex a complete cerebral cortex segmentation and reconstruction pipeline has been developed. Segmentation of the neonatal cortex is not effectively done by existing algorithms designed for the adult brain because the contrast between grey and white matter is reversed. This causes pixels containing tissue mixtures to be incorrectly labelled by conventional methods. The neonatal cortical segmentation method that has been developed is based on a novel expectation-maximization (EM) method with explicit correction for mislabelled partial volume voxels. Based on the resulting cortical segmentation, an implicit surface evolution technique is adopted for the reconstruction of the cortex in neonates. The performance of the method is investigated by performing a detailed landmark study. To facilitate study of cortical development, a cortical surface registration algorithm for aligning the cortical surface is developed. The method first inflates extracted cortical surfaces and then performs a non-rigid surface registration using free-form deformations (FFDs) to remove residual alignment. Validation experiments using data labelled by an expert observer demonstrate that the method can capture local changes and follow the growth of specific sulcus

Validating a neural network-based online adaptive system

Neural networks are popular models used for online adaptation to accommodate system faults and recuperate against environmental changes in real-time automation and control applications. However, the adaptivity limits the applicability of conventional verification and validation (V&V) techniques to such systems. We investigated the V&V of neural network-based online adaptive systems and developed a novel validation approach consisting of two important methods. (1) An independent novelty detector at the system input layer detects failure conditions and tracks abnormal events/data that may cause unstable learning behavior. (2) At the system output layer, we perform a validity check on the network predictions to validate its accommodation performance.;Our research focuses on the Intelligent Flight Control System (IFCS) for NASA F-15 aircraft as an example of online adaptive control application. We utilized Support Vector Data Description (SVDD), a one-class classifier to examine the data entering the adaptive component and detect potential failures. We developed a decompose and combine strategy to drastically reduce its computational cost, from O(n 3) down to O( n32 log n) such that the novelty detector becomes feasible in real-time.;We define a confidence measure, the validity index, to validate the predictions of the Dynamic Cell Structure (DCS) network in IFCS. The statistical information is collected during adaptation. The validity index is computed to reflect the trustworthiness associated with each neural network output. The computation of validity index in DCS is straightforward and efficient.;Through experimentation with IFCS, we demonstrate that: (1) the SVDD tool detects system failures accurately and provides validation inferences in a real-time manner; (2) the validity index effectively indicates poor fitting within regions characterized by sparse data and/or inadequate learning. The developed methods can be integrated with available online monitoring tools and further generalized to complete a promising validation framework for neural network based online adaptive systems

Forecasting of financial data: a novel fuzzy logic neural network based on error-correction concept and statistics

First, this paper investigates the effect of good and bad news on volatility in the BUX return time series using asymmetric ARCH models. Then, the accuracy of forecasting models based on statistical (stochastic), machine learning methods, and soft/granular RBF network is investigated. To forecast the high-frequency financial data, we apply statistical ARMA and asymmetric GARCH-class models. A novel RBF network architecture is proposed based on incorporation of an error-correction mechanism, which improves forecasting ability of feed-forward neural networks. These proposed modelling approaches and SVM models are applied to predict the high-frequency time series of the BUX stock index. We found that it is possible to enhance forecast accuracy and achieve significant risk reduction in managerial decision making by applying intelligent forecasting models based on latest information technologies. On the other hand, we showed that statistical GARCH-class models can identify the presence of leverage effects, and react to the good and bad news.Web of Science421049

Health care intelligent system: A neural network based method for early diagnosis of Alzheimer\u27s disease using MRI images

Alzheimer\u27s disease (AD) is a neurodegenerative disease that causes memory loss and is considered the most common type of dementia. In many countries, AD is commonly affecting senior citizens having an aged more than 65 years. Machine learning-based approaches have some limitations due to data pre-processing issues. We propose a health care intelligent system based on a deep convolutional neural network (DCNN) in this research work. It classifies normal control (NC), mild cognitive impairment (MCI), and AD. The proposed model is employed on white matter (WM), and grey matter (GM) tissues with more cognitive decline features. In the experimental process, we used 375 Magnetic Resonance Image (MRI) subjects collected from Alzheimer\u27s disease neuroimaging initiative (ADNI), including 130 NC people, 120 MCI patients, and 125 AD patients. We extract three major regions during pre-processing, that is, WM, GM and cerebrospinal fluid (CSF). This study shows promising classification results for NC versus AD 97.94%, MCI versus AD 92.84%, and NC versus MCI 88.15% on GM images. Furthermore, our proposed model attained 95.97%, 90.82%, and 86.87% on the same three binary classes on WM tissue, respectively. When comparing existing studies in terms of accuracy and other evaluation parameters, we found that our proposed approach shows better results than those approaches based on the CNN method

Hyperspectral imagery combined with machine learning to differentiate genetically modified (GM) and non-GM canola

Canola, also known as rapeseed (Brassica napus L.), is an oilseed that produces a healthy food-grade oil, canola meal by-product, and biofuel. It is the fourth most grown grain in Australia. Genetically modified (GM) canola currently represents approximately twenty percent of national canola production; hence, with clashing public and industry perceptions of genetically modified organisms (GMOs), transparency and traceability must be enabled throughout the supply chain to protect markets and relationships with consumers. GM canola must not cross-contaminate non-GM canola as our largest export market, Europe, has extremely strict protocols on GMOs. GM and non-GM canola cannot be differentiated by the human eye, with polymerase chain reaction (PCR) methods currently the main alternative, which is expensive and time-consuming. This thesis evaluates the potential to differentiate GM from non-GM canola using the novel, rapid, and non-destructive technique of hyperspectral imaging combined with machine learning. Hyperspectral imagery captures and processes wavelengths beyond simply red, green, and blue. It has a pre-existing multitude of uses including the characterisation and variety identification of other grains. In this study 500 images each of non-GM and GM canola seeds were captured. Seeds were placed on a black background with two lights sources. Images were captured from the 400nm to 1000nm wavelengths, a total of 80 bands, at a 25-millisecond exposure time. These images were run through a convolutional neural network in Keras for analysis. The high dynamic range and raw files were combined into a NumPy file for the hyperspectral image generator. Contrary to expectations, however, the models using the bitmap image files performed similarly to the models receiving the hyperspectral images. Regardless, both produced high validation accuracies around 90%, indicating a detectable phenotypical difference between the two, and further studies could lead to the development of a new approach to GM canola detection

Scoring pleurisy in slaughtered pigs using convolutional neural networks

Diseases of the respiratory system are known to negatively impact the profitability of the pig industry, worldwide. Considering the relatively short lifespan of pigs, lesions can be still evident at slaughter, where they can be usefully recorded and scored. Therefore, the slaughterhouse represents a key check-point to assess the health status of pigs, providing unique and valuable feedback to the farm, as well as an important source of data for epidemiological studies. Although relevant, scoring lesions in slaughtered pigs represents a very time-consuming and costly activity, thus making difficult their systematic recording. The present study has been carried out to train a convolutional neural network-based system to automatically score pleurisy in slaughtered pigs. The automation of such a process would be extremely helpful to enable a systematic examination of all slaughtered livestock. Overall, our data indicate that the proposed system is well able to differentiate half carcasses affected with pleurisy from healthy ones, with an overall accuracy of 85.5%. The system was better able to recognize severely affected half carcasses as compared with those showing less severe lesions. The training of convolutional neural networks to identify and score pneumonia, on the one hand, and the achievement of trials in large capacity slaughterhouses, on the other, represent the natural pursuance of the present study. As a result, convolutional neural network-based technologies could provide a fast and cheap tool to systematically record lesions in slaughtered pigs, thus supplying an enormous amount of useful data to all stakeholders in the pig industry

Multilayer optical learning networks

A new approach to learning in a multilayer optical neural network based on holographically interconnected nonlinear devices is presented. The proposed network can learn the interconnections that form a distributed representation of a desired pattern transformation operation. The interconnections are formed in an adaptive and self-aligning fashioias volume holographic gratings in photorefractive crystals. Parallel arrays of globally space-integrated inner products diffracted by the interconnecting hologram illuminate arrays of nonlinear Fabry-Perot etalons for fast thresholding of the transformed patterns. A phase conjugated reference wave interferes with a backward propagating error signal to form holographic interference patterns which are time integrated in the volume of a photorefractive crystal to modify slowly and learn the appropriate self-aligning interconnections. This multilayer system performs an approximate implementation of the backpropagation learning procedure in a massively parallel high-speed nonlinear optical network

Graph Neural Network-based surrogate model for granular flows

Accurate simulation of granular flow dynamics is crucial for assessing various geotechnical risks, including landslides and debris flows. Granular flows involve a dynamic rearrangement of particles exhibiting complex transitions from solid-like to fluid-like responses. Traditional continuum and discrete numerical methods are limited by their computational cost in simulating large-scale systems. Statistical or machine learning-based models offer an alternative. Still, they are largely empirical, based on a limited set of parameters. Due to their permutation-dependent learning, traditional machine learning-based models require huge training data to generalize. To resolve these problems, we use a graph neural network, a state-of-the-art machine learning architecture that learns local interactions. Graphs represent the state of dynamically changing granular flows and the interaction laws, such as energy and momentum exchange between grains. We develop a graph neural network-based simulator (GNS) that takes the current state of granular flow and predicts the next state using Euler explicit integration by learning the local interaction laws. We train GNS on different granular trajectories. We then assess the performance of GNS by predicting granular column collapse. GNS accurately predicts flow dynamics for column collapses with different aspect ratios unseen during training. GNS is hundreds of times faster than high-fidelity numerical simulators. The model also generalizes to domains much larger than the training data, handling more than twice the number of particles than it was trained on