Pneumonia Detection and Severity Classification in Chest X-Rays through Region Based Isolation and Optimized CNN Architectures

Abstract - Globally, pneumonia is still a major health concern, particularly in areas with poor diagnostic facilities. This paper compares two CNN architectures, ConvXNet and a CustomCNN, for deep learning-based pneumonia identification using chest X-ray pictures. Preprocessing methods were used, including data augmentation, contrast enhancement, normalization, and grayscale conversion. A segmentation framework based on U-Net and ResNet32 was also implemented in order to separate lung regions and extract information unique to each region.