Abstract
Abstract: Pneumonia is one of the top 10 causes of death worldwide and the leading cause of death in young children. Chest Xray radiographs are often examined by highly qualified experts to look for pneumonia. Radiologists commonly disagree because of this laborious process. Computer-aided diagnostics systems have shown the ability to improve diagnosis accuracy. In this paper, we have given a computational technique for finding pneumonia regions using single-shot detectors, squeeze, and extinction deep convolution neural network (CNN) augmentations, and multi-task learning. One of the challenge's best results was obtained by a modified CNN model with the recommended procedure and got an accuracy of 96%, which was evaluated as part of the radiological society of North America's pneumonia detection challenge.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal for Research in Applied Science and Engineering Technology
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
