Deep learning-based automatic segmentation of COVID-19 in chest X-ray images using ensemble neural net sentinel algorithm

Abstract

Introduction: COVID-19 has affected billions of people worldwide and has serious public health effects. Automatic image segmentation is crucial to improve clinical decision-making for full assessments of illness management as well as monitoring. It plays a crucial part in detecting, diagnosing, and tracking diseases by allowing for the exact or accurate segmentation of diseased regions. Automatic segmentation and classifying medical images remains the most difficult obstacle in medical diagnosis, however, because of the lack of annotated medical images. Methods: In this article, we propose the Ensemble Neural Net Sentinel Algorithm (ENNSA), and unique deep learning (DL) method for the segmentation and classification of COVID-19. Chest X-ray dataset is first collected from Kaggle and preprocessed. The features in the processed image are retrieved using the Insistent Grey Level Co-occurrence Matrix (IGLCM). Results: The suggested ENNSA outperforms current techniques in terms of accuracy (99.25), precision (100), recall (43), and F1 score (61) regardless of unbalanced datasets, indicating its promise as a diagnostic tool for respiratory diseases. Conclusion: In addition, the segmentation approach is used to separate the chosen features for COVID-19 classification from the chest X-ray images. The investigational outcomes demonstrate that our proposed strategy outperforms currently used methods in classifying COVID-19.

Keywords

• COVID-19
• Chest X-ray
• Deep learning (DL)
• Ensemble neural net sentinel algorithm (ENNSA)