Improvised light weight deep CNN based U-Net for the semantic segmentation of lungs from chest X-rays

Abstract

In the last decade, neural networks and deep learning techniques are widely adopted in the field of medical imaging for image detection, classification, and segmentation tasks and has achieved exceptional results. Deep models have immensely contributed in this sector thus making it easier for quick diagnosis, early and effective treatment. X-Rays are most commonly used radiological medical imaging tool and radiologists. Medical practitioners face huge difficulty to classify and segment different organs by looking at the chest X-Rays for the possible detection of any abnormalities in thoracic cavity which includes lungs, heart, diaphragm, sternum, and clavicles. Specifically, we presented a modified U-Net architecture for the semantic segmentation of lungs from the chest X-Rays images. The proposed model is lighter counterpart of U-Net architecture consisting multiple dropouts in all the deconvolutional layers, this efficiently resolving the problem of overfitting and outperforms the original U-Net model. The light weight proposed architecture outperforms on the combination of three different datasets by producing 92.71% training accuracy and a marvelous generalization accuracy of 93.87% with Adam optimizer and underlying loss function is Dice coefficient. The clubbed dataset comprised of over 900 chest X-Rays images along with corresponding truth segmentation masks provided by the professionals. Conclusively, we offer improved performance, better model generalization with relatively lighter model for the semantic segmentation of lungs from the chest X-Rays images.