Automatic diagnosis of tuberculosis disease based on Plasmonic ELISA and color-based image classification

Tuberculosis (TB) remains one of the most devastating infectious diseases and its treatment efficiency is majorly influenced by the stage at which infection with the TB bacterium is diagnosed. The available methods for TB diagnosis are either time consuming, costly or not efficient. This study employs a signal generation mechanism for biosensing, known as Plasmonic ELISA, and computational intelligence to facilitate automatic diagnosis of TB. Plasmonic ELISA enables the detection of a few molecules of analyte by the incorporation of smart nanomaterials for better sensitivity of the developed detection system. The computational system uses k-means clustering and thresholding for image segmentation. This paper presents the results of the classification performance of the Plasmonic ELISA imaging data by using various types of classifiers. The five-fold cross-validation results show high accuracy rate (>97%) in classifying TB images using the entire data set. Future work will focus on developing an intelligent mobile-enabled expert system to diagnose TB in real-time. The intelligent system will be clinically validated and tested in collaboration with healthcare providers in Malaysia

Automatic diagnosis of tuberculosis disease based on plasmonic ELISA and color-based image classification

Tuberculosis (TB) remains one of the most devastating infectious diseases and its treatment efficiency is majorly influenced by the stage at which infection with the TB bacterium is diagnosed. The available methods for TB diagnosis are either time consuming, costly or not efficient. This study employs a signal generation mechanism for biosensing, known as Plasmonic ELISA, and computational intelligence to facilitate automatic diagnosis of TB. Plasmonic ELISA enables the detection of a few molecules of analyte by the incorporation of smart nanomaterials for better sensitivity of the developed detection system. The computational system uses k-means clustering and thresholding for image segmentation. This paper presents the results of the classification performance of the Plasmonic ELISA imaging data by using various types of classifiers. The five-fold cross-validation results show high accuracy rate (>97%) in classifying TB images using the entire data set. Future work will focus on developing an intelligent mobile-enabled expert system to diagnose TB in real-time. The intelligent system will be clinically validated and tested in collaboration with healthcare providers in Malaysia

Modelling synaptic loss, compensation mechanisms and neural oscillations in Alzheimer's Disease

Alzheimer’s disease (AD) is a devastating brain disease that leads to a gradual loss of mental functions. The level of mental activity decline in AD is best correlated with the degree of synaptic loss. The latter is secondary to aggregations of two harmful proteins: neurofibrillary tangles (NFT) and beta-amyloid (Aβ) peptide. Such factors impair inter-neuronal communication thereby weakening the electrical activity of the brain. Non-invasive recording of the electrical activity along the brain scalp, known as Electroencephalography (EEG), reveals an overall power content shift towards the lower bands of the frequency domain in AD. Computational modelling studies have investigated biomarkers of EEG abnormalities in AD with the aim of a better understanding of altered neural processes in AD. The findings of these studies suggested that connectivity loss in the thalamocortical system, impaired production of the acetylcholine (ACh) neurotransmitter and deregulation of neuronal ionic channels are possible biomarkers of abnormal EEG activity in AD. This thesis presents a detailed investigation into the neural causes underpinning abnormal oscillatory activity in AD. The effects of excitatory neuronal death on beta band power (13-30 Hz) are assessed using a conductance-based network model of 200 neurons. Neurobiological studies have shown that cortical neuronal death is mediated by dysfunctional ionic behaviour. This work investigates the influence of deregulated negative feedback to the membrane potential of cortical neurons on the oscillatory activity of a cortical network model of 1000 neurons. Furthermore, a large-scale neuronal model with important characteristics is developed for the purpose of studying the interplay between various synaptic degeneration and compensation mechanisms. and abnormal oscillatory activity in AD. Finally, the influence of compensation mechanisms on the lesioned thalamocortical network is investigated with an improved neuronal model. The ultimate goal of the thesis is to provide insights for drug design in AD therapy and to contribute to the prevention measures of AD.EThOS - Electronic Theses Online ServiceGBUnited Kingdo