Detection and Segmentation of Lungs Regions Using CNN Combined with Levelset

Abstract

Lung diseases are among the leaders in ranking diseases that kill the most globally. A quick and accurate diagnosis made by a specialist doctor facilitates the treatment of the disease and can save lives. In recent decades, an area that has gained strength in computing has been the aid to medical diagnosis. Several techniques were created to help health professionals in their work using Computer Vision Techniques and Machine Learning. This work presents a method of lung segmentation based on deep learning and computer vision techniques to aid in the medical diagnosis of lung diseases. The method uses the Detectron2 convolutional neural network for detection, which obtained 99.89% accuracy for detecting the pulmonary region. It was then combined with the LevelSet method for segmentation, which got 99.32% accuracy in segmentation in Lung Computed Tomography images being equivalent in state of the art, surpassing different deep learning models for segmentation.