CovSeg-Unet: End-to-End Method-based Computer-Aided Decision Support System in Lung COVID-19 Detection on CT Images

Abstract

COVID-19 epidemic continues to threaten public health with the appearance of new, more severe mutations, and given the delay in the vaccination process, the situation becomes more complex. Thus, the implementation of rapid solutions for the early detection of this virus is an immediate priority. To this end, we provide a deep learning method called CovSeg-Unet to diagnose COVID-19 from chest CT images. The CovSeg-Unet method consists in the first time of preprocessing the CT images to eliminate the noise and make all images in the same standard. Then, CovSeg-Unet uses an end-to-end architecture to form the network. Since CT images are not balanced, we propose a loss function to balance the pixel distribution of infected/uninfected regions. CovSeg-Unet achieved high performances in localizing COVID-19 lung infections compared to others methods. We performed qualitative and quantitative assessments on two public datasets (Dataset-1 and Dataset-2) annotated by expert radiologists. The experimental results prove that our method is a real solution that can better help in the COVID-19 diagnosis process.

Clinical decision support systems (CDSS) in assistance to COVID‐19 diagnosis: A scoping review on types and evaluation methods

“…These studies also recommended the use of CDSS in future research. Finally, 68 articles met all the inclusion criteria 5,17,34–99 . The flowchart of the selection process is shown in Figure 1.…”

“…Types of CDSS to assist in diagnosing COVID‐19 are shown in Figure 4. Most of the studies used ICDSS based on ML (nonknowledge‐based CDSS) ( n = 52 [76.5%]) 34–85 . In these studies, the most common methods for designing CDSS were CNN ( n = 33), 38,40–42,45–47,49–52,54,56–69,71,72,78,82–85 SVM ( n = 8), 35,36,39,43,44,54,56,57 RF ( n = 7), 34,35,37,39,42,44,55 and KNN ( n = 7) 36,37,39,42,43,55,56 (Table 1 and Appendix ).…”

“…Most studies used a combination of performance metrics to evaluate CDSS ( n = 40) (Appendix ). In most of these studies, accuracy ( n = 43), 35–40,42–47,49–69,71,72,76–78,80–83,85 sensitivity (recall) ( n = 35) 35–37,39,41–44,46,47,49–54,56,57,61,63–69,71,72,76,78,81–85 and F1‐score ( n = 26) 34–36,39,41–43,46,47,50,52–54,56,57,60,61,63,64,66,68,69,81,82,84,85 were used (Table 2). Other metrics for evaluating ICDSS based on ML were the Matthews correlation coefficient (MCC), 52,56,58 receiver operating characteristic (ROC) curve, 34,36,70 and so on (Appendix ).…”

“…The most frequently used method for this purpose was the ICDSS based on ML (nonknowledge‐based CDSS), followed by ICDSS based on ES (knowledge‐based CDSS). Most studies have indicated that using CDSS have positively impacted the accurate diagnosis of COVID‐19 5,17,34–62,64–84,86–90,92–99 …”

A Comprehensive Review of Machine Learning Used to Combat COVID-19

“…Research in [ 98 ] aimed to contribute a decision-aiding tool that can detect COVID-19 in CT images. The images are preprocessed first by a random window level between −500 and −600 HU, and a window width of 1200 HU.…”