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

Abstract

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.