Automated Detection of Pneumonia from Digital Chest Radiographs Using Convolutional Neural Networks | Chapter 1 | Science and Technology - Recent Updates and Future Prospects Vol. 4

 In the field of medical image processing, neural networks are frequently utilized for automating analysis and classification tasks. Diagnosing pneumonia from CT or X-ray scans is challenging due to subtle symptoms, highlighting the need for objective and automated diagnosis in medical imaging. Given the significant global burden of pneumonia-related mortality, addressing this challenge is paramount.

This chapter aims to propose a solution using advanced deep neural network architecture. The innovation lies in integrating residual blocks with down-sampling and convolutions in the convolutional segment of the network. The approach was trained and evaluated on labelled images from NIH datasets, focusing on anterior-posterior chest X-ray images of patients aged one to twelve from Third I Imaging, a diagnostic centre in India.

Our network's results demonstrate competitiveness with state-of-the-art models such as SVM, Decision Trees, Random Forests, and UNET, achieving 97.5% accuracy, 96.6% F Score, and 0.08 Error Rate. This research contributes to improving automated pneumonia diagnosis, addressing a critical need in medical imaging.

 

Author(s)details:-

 

Dr. B. Sarada (Professor & HoD)
Ramachandra College of Engineering (A), Eluru, Andhra Pradesh, India.

 

Please See the book here :- https://doi.org/10.9734/bpi/strufp/v4/424