LeDNet: Localization-enabled Deep Neural Network for Multi-Label Radiography Image Classification

TL;DR

LeDNet enhances multi-label radiography image classification by integrating localization to focus on lung regions, improving disease detection accuracy in chest X-ray images.

Contribution

This paper introduces LeDNet, a novel approach combining localization and deep learning to improve thoracic disease classification accuracy.

Findings

Localization improves classification accuracy.

Mask overlay images outperform original images.

Enhanced focus on lung regions reduces false positives.

Abstract

Multi-label radiography image classification has long been a topic of interest in neural networks research. In this paper, we intend to classify such images using convolution neural networks with novel localization techniques. We will use the chest x-ray images to detect thoracic diseases for this purpose. For accurate diagnosis, it is crucial to train the network with good quality images. But many chest X-ray images have irrelevant external objects like distractions created by faulty scans, electronic devices scanned next to lung region, scans inadvertently capturing bodily air etc. To address these, we propose a combination of localization and deep learning algorithms called LeDNet to predict thoracic diseases with higher accuracy. We identify and extract the lung region masks from chest x-ray images through localization. These masks are superimposed on the original X-ray images to create the mask overlay images. DenseNet-121 classification models are then used for feature selection to retrieve features of the entire chest X-ray images and the localized mask overlay images. These features are then used to predict disease classification. Our experiments involve comparing classification results obtained with original CheXpert images and mask overlay images. The comparison is demonstrated through accuracy and loss curve analyses.