  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.
