An implementation of a Smart System based on Deep Learning for Pneumonia Infection Detection

Ali, Hesham A.; Osama, Rana; Mohamed, Raneem; Mahmoud, Esraa; Abd-Alhalem, Samia M.; Takieldeen, Ali E.

doi:10.21608/dusj.2023.318658

An implementation of a Smart System based on Deep Learning for Pneumonia Infection Detection

Document Type : Original research papers

Authors

¹ Faculty of Artificial Intelligence, Delta University for Science and Technology, Gamasa, 35712, Egypt. Computers and Control Systems Engineering Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt.

² Faculty of Artificial Intelligence, Delta University for Science and Technology, Gamasa, 35712, Egypt.

³ Faculty of Artificial Intelligence, Delta University for Science and Technology, Gamasa, 35712, Egypt

10.21608/dusj.2023.318658

Abstract

Pneumonia is a serious respiratory infection that can lead to severe health complications if not detected and treated early. In this paper, we propose a smart system based on deep learning for pneumonia infection detection. The system will be deployed as a web app that can receive chest x-ray images uploaded by users and return a prediction of whether the x-ray injured or no. Through the proposed smart Web application, anybody and anywhere may now access the model. There was no need for specialized knowledge, the system uses a convolutional neural network (CNN) to analyze chest X-ray images and identify signs of pneumonia infection. The CNN is trained on a large dataset of chest X-ray images labeled as either normal or infected with pneumonia. The system can be integrated into existing healthcare systems to provide early detection and timely treatment of pneumonia infections, thereby improving patient outcomes and reducing healthcare costs.

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