Automatic Lung Segmentation in CT Images Using Mask R-CNN for Mapping the Feature Extraction in Supervised Methods of Machine Learning

According to the World Health Organization the automatic segmentation of lung images is a major challenge in the processing and analysis of medical images, as many lung pathologies are classified as severe and such conditions bring about 250,000 deaths each year and by 2030 it will be the third leading cause of death in the world. Mask R-CNN is a recent and excellent Convolutional Neural Network model for object detection, localization, segmentation of natural image object instances. In this study, we created a new feature extractor that functions as Mask R-CNN kernel for lung image segmentation, yielding highly effective and promising results. Bringing a new approach to your training that significantly minimizes the number of images used by the Convolutional Network in its training to generate good results, thereby also decreasing the number of interactions performed by network learning. The model obtained results evidently surpassing the standard results generated by Mask R-CNN.