  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.
