ADVANCES AND CHALLENGES IN DEEP LEARNING FOR MEDICAL IMAGING: A COMPREHENSIVE SURVEY AND CASE STUDIES



Authors

  • Fareesa Khan1, Afshan Shah2, Aqib Anees3, Mohammad Ali4, Shah Zaman Nizamani5

DOI:

https://doi.org/10.15282/jmes.17.1.2023.10.0759


Keywords:

Deep learning, medical imaging, Convolutional Neural Networks (CNNs), advancements, challenges.


Abstract

In medical imaging, deep learning is a game-changing technique. It makes it possible to accurately analyze complex medical data. We investigate the developments and difficulties of deep learning for medical imaging. We do this in an extensive survey and case studies. These algorithms are very accurate in X-ray, MRI, and CT abnormality detection. They work by using Convolutional Neural Networks (CNNs) and other cutting-edge methods. Deep learning in medical imaging has many obstacles. These include its huge potential. But, also its interpretability, privacy, regulations, and ethics. We show the practical uses of deep learning in medical imaging. We do this through case studies and real-world examples. We emphasize its potential to transform healthcare delivery and improve patient outcomes. The study aims to help researchers, doctors, and policymakers. It will cover the current state of the field. It will address future directions for using deep learning in medical imaging.



Published

2024-03-11

How to Cite