One type of deep neural network that is particularly useful for evaluating visual imagery is called a convolutional neural network, or CNN. They are made up of several layers of neurons arranged into three primary categories: completely linked layers, pooling layers, and convolutional layers. One family of deep neural networks called convolutional neural networks (CNNs) is mostly used for the analysis of visual imagery. They are made up of several layers of neurons arranged into three primary categories: completely linked layers, pooling layers, and convolutional layers.
Published in Chapter:
Introduction to AI in Biomedical and Biotechnology
R. K. Chaurasia (The ICFAI University, Jaipur, India), Vaibhav Maheswari (The ICFAI University, Jaipur, India), and
A. K. Saini (The ICFAI University, Jaipur, India)
Copyright: © 2024
|Pages: 20
DOI: 10.4018/979-8-3693-3629-8.ch002
Abstract
The infusion of biomedical and bio-technology is gaining high visibility as an asset for estimating various health problems at a fast pace as well as making it less expensive than the earlier methodologies. Despite the challenges, AI is very helpful in the near future in many ways such as early detection and diagnoses of a disease, providing more effective and personalized treatment options, reducing the healthcare cost, and improving the resource allocation. AI algorithms are also being used to analyze x-rays, CT scans, and other images to detect disease earlier with a great accuracy which leads to improved health outcomes of the patient. AI also analyzes massive genomic data to recognize infected genes, predict infection risk, and develop personalised therapies. By this, it can be concluded that the infusion of AI in biomedical and biotechnical has pushed healthcare into a transformative era.