A study on the Deep Learning Models for Lung Disease Detection from Chest X-Ray Images

Chest X-ray (CXR) imaging is a popular,
inexpensive, and non-invasive diagnostic method for examining
lung diseases. However, even radiologists often have lower
accuracy with CXR data than more expensive methods like CT
or MRI, due to lower precision of CXR images. Here, we
present a new hybrid deep learning model of image features
obtained through a ResNet50 model and clinical data taken
through a Random Forest (RF) classifier. The image processing
pipeline involved scaling, background/foreground noise
removal, feature augmentation, and data cleaning for the
clinical data, to present robust input to both deep learning
model and RF classifier. The deep learning image features
obtained from the ResNet50 model were fused with clinical
data obtained from the RF classifier to complete a final
classification. Our results compared model levels of accuracy,
precision, recall, and F1-score against modern CNN and RNN
baselines. Our experiments on a curated and classified CXR
data set demonstrated that our ResNet50+RF hybrid model
achieved the best performance, with 92% accuracy, 95.9%
precision, 96% recall, and a 96% F1-score; the CNN model
achieved an accuracy of 91%. RNN performance was lower
overall, with an accuracy of 89%. These conclusions suggest
the potential of extracting deep features from an image, while
also performing ensemble learning on clinical data to improve
classification accuracy with a scalable model for real-world
clinical decision support systems.