ABSTRACT
This book discusses the applications and optimization of emerging smart technologies in the field of healthcare. It further explains different modeling scenarios of the latest technologies in the healthcare system and compares the results to better understand the nature and progress of diseases in the human body, which would ultimately lead to early diagnosis and better treatment and cure of diseases with the help of distributed technology. Covers the implementation models using technologies such as artificial intelligence, machine learning, and deep learning with distributed systems for better diagnosis and treatment of diseases. Gives in-depth review of technological advancements like advanced sensing technologies such as plasmonic sensors, usage of RFIDs, and electronic diagnostic tools in the field of healthcare engineering. Discusses possibilities of augmented reality and virtual reality interventions for providing unique solutions in medical science, clinical research, psychology, and neurological disorders. Highlights the future challenges and risks involved in the application of smart technologies such as cloud computing, fog computing, IOT, and distributed computing in healthcare. Confers to utilize the AI and ML and associated aids in healthcare sectors in the post-Covid 19 period to revitalize the medical setup. Contributions included in the book will motivate technological developers and researchers to develop new algorithms and protocols in the healthcare field. It will serve as a vast platform for gaining knowledge regarding healthcare delivery, health- care management, healthcare in governance, and health monitoring approaches using distributed environments. It will serve as an ideal reference text for graduate students and researchers in diverse engineering fields including electrical, electronics and communication, computer, and biomedical fields. © 2023 selection and editorial matter, Preeti Nagrath, Jafar A. Alzubi, Bhawna Singla, Joel J. P. C. Rodrigues and A. K. Verma;individual chapters, the contributors.
ABSTRACT
Introduction: COVID-19 is a pandemic disease affecting the global mankind since December 2019. Diagnosing COVID-19 using lung X-ray image is a great challenge faced by the entire world. Early detection helps the doctors to suggest suitable treatment and also helps speedy recovery of the patients. Advancements in the field of computer vision aid medical practitioners to predict and diagnosis disease accurately. Objective: This study aims to analyze the chest X-ray for determining the presence of COVID-19 using machine learning algo-rithm. Methods: Researchers propose various techniques using machine learning algorithms and deep learning approaches to de-tect COVID-19. However, obtaining an accurate solution using these AI techniques is the main challenge still remains open to researchers. Results: This paper proposes a Local Binary Pattern technique to extract discriminant features for distinguishing COVID-19 disease using the X-ray images. The extracted features are given as input to various classifiers namely Random Forest (RF), Linear Discriminant Analysis (LDA), k-Nearest Neighbour (kNN), Classification and Regression Trees (CART), Support Vector Machine (SVM), Linear Regression (LR), and Multi-layer perceptron neural network (MLP). The proposed model has achieved an accuracy of 77.7% from Local Binary Pattern (LBP) features coupled with Random Forest classifier. Conclusion: The proposed algorithm analyzed COVID X-ray images to classify the data in to COVID-19 or not. The features are extracted and are classified using machine learning algorithms. The model achieved high accuracy for linear binary pattern with random forest classifier. © IJCRR.