Detection of Blood-Related Diseases Using Deep Neural Nets

View Sample PDF

Author(s): Rajithkumar B. K. (R. V. College of Engineering, India), Shilpa D. R. (R. V. College of Engineering, India)and Uma B. V. (R. V. College of Engineering, India)
Copyright: 2019
Pages: 15
Source title: Handbook of Research on Deep Learning Innovations and Trends
Source Author(s)/Editor(s): Aboul Ella Hassanien (Cairo University, Egypt), Ashraf Darwish (Helwan University, Egypt)and Chiranji Lal Chowdhary (VIT University, India)
DOI: 10.4018/978-1-5225-7862-8.ch001

Keywords: Artificial Intelligence / Computer Science & IT / Engineering Science Reference / Neural Networks

Purchase

View on the publisher's website for pricing and purchasing information.

Abstract

Image processing offers medical diagnosis and it overcomes the shortcomings faced by traditional laboratory methods with the help of intelligent algorithms. It is also useful for remote quality control and consultations. As machine learning is stepping into biomedical engineering, there is a huge demand for devices which are intelligent and accurate enough to target the diseases. The platelet count in a blood sample can be done by extrapolating the number of platelets counted in the blood smear. Deep neural nets use multiple layers of filtering and automated feature extraction and detection and can overcome the hurdle of devising complex algorithms to extract features for each type of disease. So, this chapter deals with the usage of deep neural networks for the image classification and platelets count. The method of using deep neural nets has increased the accuracy of detecting the disease and greater efficiency compared to traditional image processing techniques. The method can be further expanded to other forms of diseases which can be detected through blood samples.

The IRMA Community

Research IRM

Detection of Blood-Related Diseases Using Deep Neural Nets

Purchase

Abstract

Related Content

IRMA Sponsors