Development of hippocampus MRI image segmentation algorithm for progression detection of alzheimer’s disease (AD)

Alzheimer's disease is becoming one of the most serious ailments that people face. Alzheimer's disease primarily affects those over the age of 65. is defined by the death of brain cells, which results in memory loss. as well as a lack of judgment, linguistic abilities, and decision-making capability Furthermore, no research has been conducted on developing a monitoring system for Alzheimer's disease that can continuously monitor Alzheimer's patients to identify any signs of development. Current research focuses mostly on early diagnosis and does not include disease monitoring. Monitoring is critical since it allows doctors to assess the disease development of Alzheimer's patients quantitatively. This study indicates developing an algorithm for detecting and progressing through the hippocampus of patients with Alzheimer's disease in MRI images. The active contour method (Chan-Vese) was utilized to extract the ROI parameters (hippocampus). The active contours algorithm deforms an item's initial border in an image to latch onto typical features inside the region of interest given an approximation of the object's perimeter. This is constantly stretched until it reaches the ROI's boundary. The interactive area selection approach is used to allow the user to determine the ROI depending on their needs. The algorithm will be applied once the ROI has been specified. The algorithm will be able to identify the parameters, such as the number of pixels, area pixels, and mean value, by extracting the hippocampal shape. The extraction of parameters will allow us to determine the extent of the patient's Alzheimer's progression. As a result, the study was successful in developing a semi-automated and robust model based on the Chan-Vese segmentation methodology, where it could observe the shrinking of the patient brain by the progression method using the total pixels of the hippocampus and its area by getting decreased at the second visit, one of the results showed at the first visit the total number of the pixels was 707 then at the second visit it shows 650 so the progression percentage 9%, and the proposed method produced promising segmentation results. In addition, a graphical user interface (GUI) was created to identify the progression percentage. As a future plan, this project can use machine learning to train the data for auto-detection for the hippocampus which will be significantly robust and more effective

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

Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research

This survey presents a comprehensive review of current literature on Explainable Artificial Intelligence (XAI) methods for cyber security applications. Due to the rapid development of Internet-connected systems and Artificial Intelligence in recent years, Artificial Intelligence including Machine Learning and Deep Learning has been widely utilized in the fields of cyber security including intrusion detection, malware detection, and spam filtering. However, although Artificial Intelligence-based approaches for the detection and defense of cyber attacks and threats are more advanced and efficient compared to the conventional signature-based and rule-based cyber security strategies, most Machine Learning-based techniques and Deep Learning-based techniques are deployed in the “black-box” manner, meaning that security experts and customers are unable to explain how such procedures reach particular conclusions. The deficiencies of transparencies and interpretability of existing Artificial Intelligence techniques would decrease human users’ confidence in the models utilized for the defense against cyber attacks, especially in current situations where cyber attacks become increasingly diverse and complicated. Therefore, it is essential to apply XAI in the establishment of cyber security models to create more explainable models while maintaining high accuracy and allowing human users to comprehend, trust, and manage the next generation of cyber defense mechanisms. Although there are papers reviewing Artificial Intelligence applications in cyber security areas and the vast literature on applying XAI in many fields including healthcare, financial services, and criminal justice, the surprising fact is that there are currently no survey research articles that concentrate on XAI applications in cyber security. Therefore, the motivation behind the survey is to bridge the research gap by presenting a detailed and up-to-date survey of XAI approaches applicable to issues in the cyber security field. Our work is the first to propose a clear roadmap for navigating the XAI literature in the context of applications in cyber security

Efficient Decision Support Systems

This series is directed to diverse managerial professionals who are leading the transformation of individual domains by using expert information and domain knowledge to drive decision support systems (DSSs). The series offers a broad range of subjects addressed in specific areas such as health care, business management, banking, agriculture, environmental improvement, natural resource and spatial management, aviation administration, and hybrid applications of information technology aimed to interdisciplinary issues. This book series is composed of three volumes: Volume 1 consists of general concepts and methodology of DSSs; Volume 2 consists of applications of DSSs in the biomedical domain; Volume 3 consists of hybrid applications of DSSs in multidisciplinary domains. The book is shaped decision support strategies in the new infrastructure that assists the readers in full use of the creative technology to manipulate input data and to transform information into useful decisions for decision makers

Atlas based image reconstruction for diffuse optical imaging of the human brain

Diffuse Optical Tomography (DOT) has been applied to imaging functional activations in the adult brain. Registered-atlas models are acceptable alternative forward models for the subject-specific models. In this work, different landmark-based rigid registration methods are quantitatively evaluated and compared in geometrical accuracy of the registration result, accuracy of light propagation approximation and recovery accuracy of the brain activations based on the whole head and localized head regions. The most suitable registration methods are selected based on accuracy and efficiency and they vary based on region of interest. For example, the most suitable registration method for recovery of whole cortex activation is the registration method based on 19 landmarks from the EEG 10/20 system and non-iterative Point to Point algorithms (EEG19nP2P registration). Efficiency of the recovery process is another popular research area in DOT. In this work, a modified generation approach of the light propagation approximation is designed based on a reduced sensitivity matrix and parallelisation process. It improves the storage efficiency by >1000% and time efficiency by ~400%. Based on this approach, the brain activation recovery of DOT can be processed on a normal laptop without large memory requirements within 45 minutes which is more suitable for a portable system