Presents the procedures followed to screen COVID-19

 Chapter 1 presents an overview of the disease screening process followed in hospitals to analyze and confirm disease in various internal and external human organs. This section also presents the procedures followed to screen COVID-19 infected patients during disease diagnosis and confirmation. Further, this section presents a detailed discussion regarding the image recording procedures followed in hospitals to analyze the disease. 

Chapter 2 demonstrates the need for image enhancement procedures with appropriate experimental results obtained with MATLAB. The need for improvement is outlined briefly by well-known methods such as artifact removal Vinyl Gloves, filtering, contrast enhancement, edge detection, thresholding, and smoothing. The recent advancement in image enhancements, such as the hybrid image assessment technique Synthetic Gloves, is also presented with experimental results. 

Chapter 3 discusses details on the choice of suitable image examination procedures that are demonstrated using appropriate results attained using MATLAB software YICHANG. Further, this chapter presents the details regarding particle swarm optimization, bacterial foraging, firefly, bat, cuckoo, social group optimization, teaching-learning, and the jaya algorithm and their role during the image thresholding process. 

Chapter 4 presents an overview of the traditional and the CNN-based segmentation procedures. This section also presents the experimental outcome of the proposed technique on greyscale and RGB images with and without noise. The performance evaluation of the proposed segmentation is also demonstrated using appropriate examples. 


 

Chapter 5 demonstrates the implementation of the hybrid image processing technique implemented to examine brain tumors using brain MRI slices. This section presents a detailed demonstration of the various traditional segmentation procedures considered to extract the tumor section from the MRI slice. 

Chapter 6 presents an overview of deep-learning architectures such as AlexNet, VGG-16, and VGG-19 and their application in medical image classification tasks. This section further discusses the transfer-learning technique and the essential modification to be implemented to enhance the classification accuracy. A detailed lung CT scan slice classification with the VGG architecture is demonstrated using the public image dataset collected from COVID-19 patients. This section presents the experimental result attained using MATLAB and Python software. 

Chapter 7 concludes the presented work in the previous chapters and discusses the future scope.

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