Improving COVID-19 Recognition: A Study of Contrast Enhancement Techniques and Normalization in Chest X-Rays Images

The analysis of chest radiographs holds significant importance in healthcare and medical diagnostics. Also known as chest X-rays, these images offer valuable insights into the condition of a patient’s chest area, including vital organs like the lungs, heart, and surrounding structures. Amid the ongoing COVID-19 pandemic, the analysis of chest radiographs has become even more crucial. These images are extensively utilized in detecting and monitoring COVID-19 pneumonia, aiding in identifying characteristic lung patterns associated with the disease. This paper presents a method for automatically detecting and normalizing the Region of Interest (ROI) in chest radiographs. It evaluates three contrast enhancement techniques: Contrast Enhancement by Particle Swarm Optimization (CE-PSO), Contrast Limited Adaptive Histogram Equalization (CLAHE), and CE-PSO+CLAHE applied to the ROI, aiming to determine the most effective method for classifying radiographs in COVID-19 detection. For this purpose, six Convolutional Neural Networks (CNNs) are evaluated, including MobileNetV2, ResNet-50, ResNet-18, Enhanced, Compact, and AlexNet. The best-performing methods were the ResNet-50 using CE-PSO, with an accuracy of 95.6%, and MobileNetV2 using CLAHE, with an accuracy of 96%. The experimental results reveal that CE-PSO+CLAHE is not recommended for image preprocessing. Furthermore, CE-PSO and CLAHE show similar results, depending on the classifier used. These results are competitive with state-of-the-art algorithms and highlight the importance of image preprocessing to classify radiographs accurately.