Medical Image Processing Using Data Science: A Review
The purpose of this work is to introduce data science in medical imaging. Both theoretical advancements and real-world applications are covered in this research work. The healthcare industry is distinct from all other industries and is a special sector. It is a top-priority industry that consumes a sizable percentage of the federal budget. In general, only doctors can analyses images, but thanks to technology, we are now able to use principles from physics, arithmetic, statistics, and other fields to process images for medical purposes. The World Health Organization (WHO) considered COVID-19 to be a major distraction for virtually the whole world as a result of its slow escalation in time, particularly for nations with poorer health infrastructure. Deep learning, data analytics, and machine learning are all applications of data science that have been used in the healthcare industry. Examples include the diagnosis of diabetes, classification of the lungs, thyroid detection, ultrasound, X-ray, and mammography, among many other sectors. This fact has spurred a huge number of medical research projects, which have been undertaken. In this study, we first summarize the most recent data science research studies and their applicability to medical image processing. Then, we give a brief overview of data science and the processing and analysis of medical images that arise from it. Finally, it provides overview of technology which has resulted a drastic change in healthcare.