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
