Exploring the Frontiers of Machine Learning in Radiology: A Comprehensive Review of Applications, Advancements, and the Challenges that Lie Ahead

Naga Theja¹,  Chaitanya^2*,  Vani Pushpa³, Sudheendra⁴, Santosh Kumar⁵ and  Sambangi Satyananda Siva⁶

¹Department of Medical Radiology and Imaging Technology, Joy University,  Vadakankulam, Tamil Nadu, India.

²Department of Anesthesia, Centurion University of Technology and Management, Vizianagaram, Andhra Pradesh, India(Corresponding Author)

³Department of Radio Diagnosis, Fathima Institute of Medical Sciences, Andhra pradesh  India.

⁴Department of Radiology and Imaging Technology, Reva University, Yelahanka, Bengaluru, Karnataka, India.

⁵Department of Radiology and Imaging Technology, Maulana Azad National Urdu University Hyderabad, Telangana, India

⁶Department of Radiology, MNR University, Telangana, India

Corresponding Author E-mail: chaitanya.chadalavada@gmail.com

ABSTRACT: By increasing production, effectiveness, and precise diagnosis, machine learning (ML) is revolutionizing the radiation therapy industry. In order to identify anomalies in different types of imaging including CT, MRI, and X-rays, this paper examines developments in machine learning (ML) approaches, especially convolutional neural networks (CNNs) and deep learning. These advancements have a great deal of promise for automation picture processing, lowering human error, and offering prompt, dependable diagnostic assistance. The requirement for sizable, exceptional datasets, the difficulties of technique validation, including ethical worries about privacy of patient information are some of the obstacles to the integration of machine learning (ML) in radiology. For ML to be widely adopted and its transformational promise in radiological imaging to be realized, these obstacles must be overcome.

KEYWORDS: CT; Machine Learning; MRI; Medical Diagnosis; X-RAY

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