Improvements in science and technology helped to develop a considerable number of medical facilities

 These improvements also provided an appropriate treatment process to control and cure diseases. Most internal organ diseases are normally assessed with the help of medical images recorded with varied modalities; hence, an appropriate image processing system is essential to detect the disease and its severity from available medical images PE Gloves. 



Improving the information obtainable in untreated digital illustration is generally performed with a selected image improvement practice Synthetic Gloves. The image improvement techniques play a vital role in analyzing a variety of imaging modalities, such as fundus retinal images, blood slides, dermoscopic images, ultrasounds, mammograms, thermal imaging, CT scan slices, X-rays, and MRI slices. Recently, image-assisted disease detection and treatment planning improved medical industries, and a considerable number of image examination schemes are proposed and implemented by researchers Disposable Gloves Wholesale. 

In medical image analysis, appropriate pre-processing and post-processing techniques are implemented to extort and appraise the disease-infected segment from the digital image. Further, the overall accuracy of this disease detection system depends on the pre-processing process. Hence, in this book, the commonly used image pre-processing technique called the ‘multi-thresholding process’ is discussed with appropriate examples. The implementation of the traditional and heuristic algorithm-based disease detection system is also discussed with appropriate examples. 

A detailed study with the hybrid image processing methods and the deep-learning based automated disease classification is also presented. Finally, the implementation of the deep-learning system is demonstrated using the lung CT scan slices of the COVID-19 dataset. In this work, the proposed work is experimentally demonstrated using MATLAB® and Python software.

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