Machine Learning for Medical Images
N. Gopinath, M. Suresh Anand, M. V. Ishwarya · Apple Academic Press eBooks · 2024
Research in medical imaging is increasingly turning to machine learning approaches. To recognize patterns in medical images, this method relies on pattern recognition. To enhance medical imaging applications, these algorithms are now being used to detect faults in the sickness diagnosing system, which may lead to significantly ambiguous medical therapy. Medical imaging relies heavily on these algorithms to detect early indications of sickness. To make predictions, it employs either supervised or unsupervised algorithms based on a predetermined dataset. Image categorization, object identification, pattern recognition, and reasoning are just a few of the principles that are examined in medical imaging. By extracting meaningful patterns for a specific condition, they are employed in medical imaging to improve accuracy. They also aid in the process of deciding on a course of action. Using Machine Learning to improve medical imaging is a trend that will continue in the future. Machine Learning approaches in medical imaging are the focus of this chapter’s discussion. Machine Learning is a must-know for medical imaging professionals. We intended to give researchers a framework for 96 understanding existing medical imaging approaches, highlighting the benefits and shortcomings of these algorithms, and discussing future prospects.