A Dual-Branch Network for Diagnosis of Thorax Diseases from Chest X-rays

Abstract

Automated chest X-ray analysis has a great potential for diagnosing thorax diseases since errors in diagnosis have always been a concern among radiologists. Being a multi-label classification problem, achieving accurate classification still remains challenging. Several studies have focused on accurately segmenting the lung regions from the chest X-rays to deal with the challenges involved. The features extracted from the lung regions typically provide precise clues for diseases like nodules. However, such methods ignore the features outside the lung regions, which have been shown to be crucial for diagnosing conditions like cardiomegaly. Therefore, in this work, we explore a dual-branch network-based framework that relies on features extracted from the lung regions as well as the entire chest X-rays. The proposed framework uses a novel network named R-I UNet for segmenting the lung regions. The dual-branch network in the proposed framework employs two pre-trained AlexNet models to extract discriminative features, forming two feature vectors. Each of these feature vectors is fed into a recurrent neural network consisting of a stack of gated recurrent units with skip connections. Finally, the resulting feature vectors are concatenated for classification. The R-I UNet has been evaluated on the JSRT and Montgomery (MC) datasets, while the dual-branch classification network has been evaluated on the NIH ChestXray14 dataset. The proposed models achieve state-of-the-art performance for both segmentation and classification tasks on the above benchmark datasets. Specifically, our lung segmentation model achieves a 5-fold cross-validation accuracy of 98.18<inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> and 99.14<inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> on MC and JSRT datasets. For classification, the proposed approach achieves state-of-the-art AUC for 9 out of 14 diseases with a mean AUC of 0.842 on NIH ChestXray14 dataset. The source code is available at <uri>https://github.com/JigneshChowdary/CXR_Classification</uri>https:<inline-formula><tex-math notation="LaTeX">$//$</tex-math></inline-formula>github.com <inline-formula><tex-math notation="LaTeX">$/$</tex-math></inline-formula> JigneshChowdary<inline-formula><tex-math notation="LaTeX">$/$</tex-math></inline-formula>CXR_Classification IEEE