IVCVLGMED-PHJun 10, 2023

Online learning for X-ray, CT or MRI

arXiv:2306.06491v16 citationsh-index: 22
Originality Synthesis-oriented
AI Analysis

This is an incremental review of AI methods for medical imaging, potentially aiding medical professionals in diagnosis.

This chapter addresses the challenge of manual disease identification in medical imaging like X-ray, CT, and MRI by exploring AI techniques to automatically detect complex patterns, aiming to improve speed and accuracy compared to traditional Computer-Aided Diagnosis systems.

Medical imaging plays an important role in the medical sector in identifying diseases. X-ray, computed tomography (CT) scans, and magnetic resonance imaging (MRI) are a few examples of medical imaging. Most of the time, these imaging techniques are utilized to examine and diagnose diseases. Medical professionals identify the problem after analyzing the images. However, manual identification can be challenging because the human eye is not always able to recognize complex patterns in an image. Because of this, it is difficult for any professional to recognize a disease with rapidity and accuracy. In recent years, medical professionals have started adopting Computer-Aided Diagnosis (CAD) systems to evaluate medical images. This system can analyze the image and detect the disease very precisely and quickly. However, this system has certain drawbacks in that it needs to be processed before analysis. Medical research is already entered a new era of research which is called Artificial Intelligence (AI). AI can automatically find complex patterns from an image and identify diseases. Methods for medical imaging that uses AI techniques will be covered in this chapter.

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