A study of lung disease using image processing in big data environment

Abstract

A In the medical field, the Image dispensation techniques are extensively employed for image amelioration in finding and treatment of lung disease in the big data environment, where the point in time feature is very paramount to determine the idiosyncrasy issues in intention images, particularly in lung disease such as cancer, pneumonia, COVID-19 etc, for early detection and treatment stages of lungs disease, Image processing technique are widely used for identification of genetic as well as environmental factors are very important in developing a novel method of lung disease prevention. The core factors of this research are quality, time, and precision of the dataset. The modification and evaluation of image quality depend on the segmentation techniques, an improved area of the object that is utilized as a rudimentary substructure of feature extraction is obtained and comparison is made on relying feature. Medical images are analyzed by different segmentation techniques of image processing. The segmentation techniques are used dataset to find patterns and retrieve information from the dataset for processing. The goal of this study is discussed various image processing techniques and big data analytics tools for lung disease has been given in the tabular form and provides comparative study. This study provides minutiae of big data analytics tools and image processing techniques, specifically discussed in the context of lung disease images.