Cloud Computing Based Network Analysis in Smart Healthcare System with Neural Network Architecture

The recent progressions in Artificial Intelligence (AI), the Internet of Things (IoT), and cloud computing transformed the traditional healthcare system into a smart healthcare system. Medical services can be improved through the incorporation of key technologies namely AI and IoT. The convergence of AI and IoT renders several openings in the healthcare system. In machine learning, deep learning can be considered a renowned topic with a wide range of applications like biomedicine, computer vision, speech recognition, drug discovery, visual object detection, natural language processing, disease prediction, bioinformatics, etc. Among these applications, medical science-related and health care applications were raised dramatically. This study develops a Cloud computing-based network analysis in the smart healthcare systems with neural network (CCNA-SHSNN) architecture. The presented CCNA-SHSNN technique assists in the decision-making process of the healthcare system in a real time cloud environment. For data pre-processing, the CCNA-SHSNN technique uses a normalization approach. Secondly, the CCNA-SHSNN technique applies the autoencoder (AE) model for healthcare data classification in the CC platform. At last, the gravitational search algorithm (GSA) is used for hyperparameter optimization of the AE model. The experimental outcomes are determined on a benchmark dataset and the outcomes signify the outperforming efficiency of the CCNA-SHSNN technique compared to existing techniques

Machine Intelligence for Advanced Medical Data Analysis: Manifold Learning Approach

In the current work, linear and non-linear manifold learning techniques, specifically Principle Component Analysis (PCA) and Laplacian Eigenmaps, are studied in detail. Their applications in medical image and shape analysis are investigated. In the first contribution, a manifold learning-based multi-modal image registration technique is developed, which results in a unified intensity system through intensity transformation between the reference and sensed images. The transformation eliminates intensity variations in multi-modal medical scans and hence facilitates employing well-studied mono-modal registration techniques. The method can be used for registering multi-modal images with full and partial data. Next, a manifold learning-based scale invariant global shape descriptor is introduced. The proposed descriptor benefits from the capability of Laplacian Eigenmap in dealing with high dimensional data by introducing an exponential weighting scheme. It eliminates the limitations tied to the well-known cotangent weighting scheme, namely dependency on triangular mesh representation and high intra-class quality of 3D models. In the end, a novel descriptive model for diagnostic classification of pulmonary nodules is presented. The descriptive model benefits from structural differences between benign and malignant nodules for automatic and accurate prediction of a candidate nodule. It extracts concise and discriminative features automatically from the 3D surface structure of a nodule using spectral features studied in the previous work combined with a point cloud-based deep learning network. Extensive experiments have been conducted and have shown that the proposed algorithms based on manifold learning outperform several state-of-the-art methods. Advanced computational techniques with a combination of manifold learning and deep networks can play a vital role in effective healthcare delivery by providing a framework for several fundamental tasks in image and shape processing, namely, registration, classification, and detection of features of interest

Intelligent image-based colourimetric tests using machine learning framework for lateral flow assays

This paper aims to deliberately examine the scope of an intelligent colourimetric test that fulfils ASSURED criteria (Affordable, Sensitive, Specific, User-friendly, Rapid and robust, Equipment-free, and Deliverable) and demonstrate the claim as well. This paper presents an investigation into an intelligent image-based system to perform automatic paper-based colourimetric tests in real-time to provide a proof-of-concept for a dry-chemical based or microfluidic, stable and semi-quantitative assay using a larger dataset with diverse conditions. The universal pH indicator papers were utilised as a case study. Unlike the works done in the literature, this work performs multiclass colourimetric tests using histogram based image processing and machine learning algorithm without any user intervention. The proposed image processing framework is based on colour channel separation, global thresholding, morphological operation and object detection. We have also deployed a server based convolutional neural network framework for image classification using inductive transfer learning on a mobile platform. The results obtained by both traditional machine learning and pre-trained model-based deep learning were critically analysed with the set evaluation criteria (ASSURED criteria). The features were optimised using univariate analysis and exploratory data analysis to improve the performance. The image processing algorithm showed >98% accuracy while the classification accuracy by Least Squares Support Vector Machine (LS- SVM) was 100%. On the other hand, the deep learning technique provided >86% accuracy, which could be further improved with a large amount of data. The k-fold cross validated LS- SVM based final system, examined on different datasets, confirmed the robustness and reliability of the presented approach, which was further validated using statistical analysis. The understaffed and resource limited healthcare system can benefit from such an easy-to-use technology to support remote aid workers, assist in elderly care and promote personalised healthcare by eliminating the subjectivity of interpretation

Developing Deep-Learning Methods for Diagnosis and Prognosis of Pediatric Progressive Diseases Using Modern Imaging Techniques

Purpose and Rationale. Central nervous system manifestations form a significant burden of disease in young children. There have been efforts to correlate the neurological disease state in tuberous sclerosis complex (TSC) neurological disease state with imaging findings is a standard part of patient care. However, such analysis of neuroimaging is time- and labor-intensive. Automated approaches to these tasks are needed to improve speed, accuracy, and availability. Automated medical image analysis tools based on 3D/2D deep learning algorithms can help improve the quality and consistency of image diagnosis and interpretation for cognitive disorders in infants. We propose to automate neuroimaging analysis with artificial intelligence algorithms. This novel approach can be used to improve the accuracy of TSC diagnosis and treatment. Deep learning (DL) is among the most successful types of machine learning and utilizes deep artificial neural networks (ANNs), which can determine efficient feature representations of input data. DL algorithms have created new opportunities in medical image analysis. Applications of DL, specifically convolutional neural networks (CNNs), in medical image analysis, cover a broad spectrum of tasks, including risk prediction/estimation with a machine learning system trained on these classification tasks. Study population. We reviewed an NIMH Data Archive (NDA) dataset that was collected in 2010. We also reviewed imaging data from patients and normal cases from birth to 8 years of age acquired at Le Bonheur Children’s Hospital from 2014 to 2020. The University of Tennessee Health Science Center Institutional Review Board (IRB) approved this study. Research Design and Study Procedures. Following Institutional Review Board (IRB) approval, this thesis: 1) Presents the first 2D/3D fusion CNN models to estimate the age of infants from birth to 3 years of age. 2) Presents the first work to look at whole-brain network to automatically distinguish TSC brain structural pathology from normal cases using a 3DCNN model. Conclusions. The study findings indicate that deep neural networks tackle the problem of early prediction of cognitive and neurodevelopmental disorders and structural brain pathology based on MRI automatically in TSC children. It is the hope of the author that analysis of MRI images via methods of deep learning will have a positive impact on healthcare for infants and children at risk of rare diseases

Energy Efficient Machine Learning-Based Classification of ECG Heartbeat Types

To meet the accuracy, latency and energy efficiency requirements during real-time collection and analysis of health data, a distributed edge computing environment is the answer, combined with 5G speeds and modern computing techniques. Using the state-of-the-art machine learning based classification techniques plays a crucial role in creating the optimal healthcare system on the edge. This thesis first provides a background on the current and emerging edge computing classification techniques for healthcare applications, specifically for electrocardiogram (ECG) beat classification. We then present key findings from an extensive survey of over hundred studies on the topic while taxonomizing the literature with respect to key architectural differences, application areas and requirements. Leveraging the insights drawn from the extensive analysis of the pertinent literature we select a set of most promising machine learning based classification techniques for ECG beats, based on their suitability for implementation on a small edge device called a Raspberry Pi. After implementing these classification techniques on a Raspberry Pi based platform we perform a comparison of the performance of these classification techniques with respect to three key performance indicators (KPI) of interest for health care applications namely accuracy, energy efficiency, and latency. ECG measures the electrical activity of the heart and help healthcare professionals to evaluate heart conditions of a patient, sometimes diagnosing life-threatening conditions. The features of ECG signals are pre-processed and fed into the classification algorithms to detect and classify abnormal beat types. ECG classification requires low complexity but still high level of performance in terms of aforementioned three KPIs. The classification algorithms chosen, namely Naïve Bayes, Multilayer Perceptron (MLP), and distilled deep neural network (DNN) are all energy efficient methods hence suitable for implementation for small edge devices. The comparative multi-faceted evaluation presented in this thesis is a new contribution to research that exists on edge based classification as it offers comparison of selected classification algorithms in terms three KPIs instead of one while using real edge device based implementation. The performance of analyzed machine learning classification techniques is ranked according to each KPI. Benefiting from the results of the comparative analysis presented in this thesis a particular classification algorithm can be selected for optimal deployment in given scenario in healthcare system depending on the specific requirements of the given scenario. Edge computing paves the way for a new generation of health devices that can offer a higher quality of life for users if low-latency, low-energy, and high- performance requirements are addressed

A Predictive Analysis of Electronic Healthcare Records for Stroke Symptoms

University of Technology Sydney. Faculty of Engineering and Information Technology.Cerebrovascular symptoms, commonly known as stroke, can affect different parts of the human body depending on the area of brain affected. The patients who survive usually have a poor quality of life because of serious illness, long-term disability and become a burden to their families and the health care system. There is a strong demand for the management focused on prevention and early treatment of disease by analysing different factors. However, a high volume of medical data, heterogeneity, and complexity have become the biggest challenges in stroke symptoms prediction. Algorithms with very high level of accuracy are, therefore, vital for medical diagnosis. The development of such algorithms nevertheless still remains obscure despite its importance and necessity for healthcare. Electronic Healthcare Records (EHRs) describe the details about patient’s physical and mental health, diagnosis, lab results, treatments or patient care plan and so forth. The huge amount of information in these records provides insights about the diagnosis and prediction of various diseases. Currently, the International Classification of Diseases, 10th Revision or ICD-10th codes is used for representing each patient record. The huge amount of information in these records provides insights about the diagnosis and prediction of various diseases. Various machine learning techniques are used for the analysis of data derived from these patient records. The predictive techniques have been widely applied in clinical decision making such as predicting occurrence of a disease or diagnosis, evaluating prognosis or outcome of diseases and assisting clinicians to recommend treatment of diseases. However, the conventional predictive models or techniques are still not effective enough in capturing the underlying knowledge because it is incapable of simulating the complexity on feature representation of the medical problem domains. This research used aggregated files of Electronic Healthcare Records (EHRs) from Department of Medical Services, The Ministry of Public Health of Thailand between 2015 and 2016. The empirical research is intended to evaluate the ability of machine learning and deep learning to recognize patterns in multi-label classification of stroke. This research aims at the investigation of five techniques: Support Vector Machine (SVM); k-Nearest Neighbours (k-NN); Backpropagation; Recurrent Neural Network (RNN); and Long Short-Term Memory - Recurrent Neural Network (LSTM-RNN). These are powerful and widely used techniques in machine learning and bioinformatics. First, we decoded ICD-10th codes into the health records, as well as other potential risk factors within EHRs into the pattern and model for prediction. Second, we purposed a conceptual Case Based Reasoning (CBR) framework for stroke disease prediction that uses previous case-based knowledge. A conceptual case-based reasoning framework to predict from patients’ health risk factors and to recognize a particular case that probably develop stroke and prepare or warn patients to handle disease burden outcome. It describes the design, implementation and evaluation of a novel system to facilitate stroke prediction, which relies on data collected from EHRs. Finally, the effectiveness of Backpropagation; RNN; and LSTM-RNN for prediction of stroke based on healthcare records is modelled. The results show several strong baselines that include accuracy, recall, and F1 measure score. Consequently, deep learning allows the disclosure of some unknown or unexpressed knowledge during prediction procedure, which is beneficial for decision-making in medical practice and provide useful suggestions and warnings to patient about unpredictable stroke