Automated Semantic Segmentation of Chest X-ray images using Deep Learning Model

ABSTRACT

Chest Radiography proves to be a faster, cheaper, and less invasive diagnosis mode for respiratory diseases like pneumonia and viral infections like the coronavirus. The utilization of AI based strategies for programmed finding or imaging are pretty prevalent. In this work, a deep learning model is proposed for automatically segmenting chest X-ray images. The model comprises 20 fully convolutional layers that simplify images to precisely section the lung lobes from the X-ray images. The utilization of transposed convolution offers a lesser computational overhead than traditional methods. The proposed model achieves an accuracy of 97%, with an average Dice coefficient of 0.95 and an average Jaccard (IoU) score of 0.90. The proposed model is trained and tested on publicly available Montgomery County (MC) and Shenzen Hospital (SH) datasets. The segmentation ability of the proposed model can be used as input for predictive models, achieving better accuracy and faster convergence. © 2021 IEEE.