Deep Residual CNN for Multi-Class Chest Infection Diagnosis
Ryan Donghan Kwon Dohyun Lim Yoonha Lee Seung Won Lee
The advent of deep learning has significantly propelled the capabilities of
automated medical image diagnosis, providing valuable tools and resources in
the realm of healthcare and medical diagnostics. This research delves into the
development and evaluation of a Deep Residual Convolutional Neural Network
(CNN) for the multi-class diagnosis of chest infections, utilizing chest X-ray
images. The implemented model, trained and validated on a dataset amalgamated
from diverse sources, demonstrated a robust overall accuracy of 93%. However,
nuanced disparities in performance across different classes, particularly
Fibrosis, underscored the complexity and challenges inherent in automated
medical image diagnosis. The insights derived pave the way for future research,
focusing on enhancing the model's proficiency in classifying conditions that
present more subtle and nuanced visual features in the images, as well as
optimizing and refining the model architecture and training process. This paper
provides a comprehensive exploration into the development, implementation, and
evaluation of the model, offering insights and directions for future research
and development in the field.