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Advancements in Medical Imaging: A Transition From Machine Learning to Deep Learning

Advancements in Medical Imaging: A Transition From Machine Learning to Deep Learning
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Author(s): Veena Grover (Noida Institute of Engineering and Technology, Greater Noida, India), Purnima Pal (Kamla Nehru Institute of Technology, Sultanpur, India)and Manju Nandal (Chandigarh University, India)
Copyright: 2024
Pages: 18
Source title: Analyzing Explainable AI in Healthcare and the Pharmaceutical Industry
Source Author(s)/Editor(s): Veena Grover (Noida Institute of Engineering and Technology, India), Balamurugan Balusamy (Manipal Academy of Higher Education, Dubai, UAE), Nallakaruppan M.K. (Vellore Institute of Technology, India), Vijay Anand (Vellore Institute of Technology, India)and Mariofanna Milanova (University of Arkansas at Little Rock, USA)
DOI: 10.4018/979-8-3693-5468-1.ch007

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Abstract

Medical imaging holds a pivotal role in modern healthcare, facilitating early disease identification, treatment planning, and patient progress monitoring. The integration of machine learning (ML) has significantly transformed medical imaging, offering automated analysis, pattern recognition, and diagnostic support. However, a notable paradigm shift has emerged in recent times, highlighting the ascendancy of deep learning (DL) techniques, heralding a new era in this field. This exploration scrutinizes the dynamic evolution within medical imaging, accentuating the departure from conventional machine learning methods toward the more advanced domain of deep learning. It scrutinizes the foundational principles of machine learning as applied in medical imaging, shedding light on the constraints that prompted the adoption of deep learning methodologies. Furthermore, the chapter explores the efficacy of deep learning models across diverse medical imaging modalities encompassing MRI, CT scans, X-rays, and ultrasound.

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