Covid-19 Lung Segmentation using U-Net CNN based on Computed Tomography Image

Abstract

In medical image analysis, lung segmentation is needed as an initial step in diagnosing various diseases in the lung area, including Covid-19 infection. Deep Learning has been used for image segmentation in recent years. One of the Deep Learning-based architectures widely used in medical image segmentation is U-Net CNN. U-Net employs a semantic segmentation approach, which has the benefit of being accurate in segmenting even though the model is trained on a limited quantity of data. Our work intends to assist radiologists in providing a more detailed visualization of COVID-19 infection on CT scans, including infection categories and lung conditions. We conduct preliminary work to segment lung regions using U-Net CNN. The dataset used is relatively small, consisting of 267 CT-scan images split into 240 (90%) images for training and 27 (10%) images for testing. The model is evaluated using the K-fold cross-validation (k=10) approach, which has been believed to be appropriate for models created with limited training data. The metric used for experiments is Mean-IoU. It is commonly used in evaluating the segmentation processes. The results achieved were satisfactory, with Mean-IoU scores ranging from 90.2% to 95.3% in each test phase (k1 – k10), with an average value of 93.3%.