Analysis for diagnosis of pneumonia symptoms using chest X-ray based on MobileNetV2 models with image enhancement using white balance and contrast limited adaptive histogram equalization (CLAHE)

Abstract

This study focuses on diagnosing pneumonia symptoms using chest X-ray (CXR) images. It employs the MobileNetV2 model alongside image enhancement techniques, including white balance and CLAHE. MobileNetV2 is a computationally efficient Convolutional Neural Network (CNN) known for its robust image recognition capabilities. White balance corrects color imbalances in CXR images, ensuring color consistency, while CLAHE enhances contrast and image details for improved analysis. The enhanced CXR images are fed into the pre-trained MobileNetV2 model, which distinguishes pneumonia and non-pneumonia cases. The study aims to enhance pneumonia diagnosis accuracy, benefiting from MobileNetV2’s efficiency and image enhancement. Notably, it achieved high accuracy and low loss for both three-class (91.17% accuracy, 35.0% loss) and two-class (99.76% accuracy, 7% loss) classifications, with the best results in the 50-epoch test. However, it is essential to consider the trade-off between computing time and the risk of overfitting when increasing epochs. Future research could explore additional features to further enhance model performance.