Detecting COVID-19 from Chest X-Ray Images using a Lightweight Deep Transfer Learning Model with Improved Contrast Enhancement Technique

• Main Authors: Bacad, Dave Jammin A, Abu, Patricia Angela R
• Format: text
• Published: Archīum Ateneo 2021
• Subjects: COVID-19; deep learning; adaptation models; pulmonary diseases; transfer learning; pipelines; lung; chest x-ray; image processing; Computer Sciences; Databases and Information Systems; Public Health; Pulmonology
• Online Access: https://archium.ateneo.edu/discs-faculty-pubs/246; https://ieeexplore.ieee.org/document/9664676
• Institution: Ateneo De Manila University

Despite the vaccinations; the emergence of new and more contagious variants of the COVID-19 disease has continued to pose threats and challenges to our lives. Until herd immunity is achieved; it is important to continuously perform screening tests to control and minimize the transmissions. Due to the reported shortcomings of the RT-PCR; the utilization of deep learning for detecting COVID-19 from Chest X-Ray (CXR) images has gathered a lot of interest from researchers. As a contribution to the field; this study proposes a deep learning pipeline that utilizes transfer learning and image enhancement techniques to classify whether a given CXR image exhibits characteristics of COVID-19 infection; pneumonia infection; or normal/healthy lungs. For a lighter approach; the small pre-trained model named EfficientNetB0 is used as the base model for the transfer learning method. To improve the network’s performance; a sequence of contrast enhancement techniques namely the Multi-Scale Retinex (MSR) and Contrast Limited Adaptive Histogram Equalization (CLAHE) is introduced in the pipeline and employed as a pre-processing step. Gathered from a 10-fold cross-validation method in a dataset with 3729 images per class; results show that the proposed approach achieves an average overall accuracy of 92.089% with 98.431% average precision; 95.119% average recall; and 96.741% average f1-score for the COVID-19 class.