Case Studies on X-Ray Imaging, MRI and Nuclear Imaging

TL;DR

This paper reviews how AI, especially CNNs, enhances disease detection in medical imaging modalities like X-ray, MRI, and nuclear imaging, addressing manual evaluation challenges and improving diagnostic accuracy.

Contribution

It provides a comprehensive overview of AI-based CNN techniques for medical image analysis, highlighting their role in improving diagnosis in X-ray, MRI, and nuclear imaging.

Findings

CNN effectively extracts features from medical images

AI techniques improve diagnostic speed and accuracy

Deep learning aids in systematic image classification

Abstract

The field of medical imaging is an essential aspect of the medical sciences, involving various forms of radiation to capture images of the internal tissues and organs of the body. These images provide vital information for clinical diagnosis, and in this chapter, we will explore the use of X-ray, MRI, and nuclear imaging in detecting severe illnesses. However, manual evaluation and storage of these images can be a challenging and time-consuming process. To address this issue, artificial intelligence (AI)-based techniques, particularly deep learning (DL), have become increasingly popular for systematic feature extraction and classification from imaging modalities, thereby aiding doctors in making rapid and accurate diagnoses. In this review study, we will focus on how AI-based approaches, particularly the use of Convolutional Neural Networks (CNN), can assist in disease detection through medical imaging technology. CNN is a commonly used approach for image analysis due to its ability to extract features from raw input images, and as such, will be the primary area of discussion in this study. Therefore, we have considered CNN as our discussion area in this study to diagnose ailments using medical imaging technology.