Enhancing Pneumonia Diagnosis and Severity Assessment through Deep Learning: A Comprehensive Approach Integrating CNN Classification and Infection Segmentation

TL;DR

The study tackles pneumonia and COVID-19 diagnosis and severity assessment from chest X-rays by proposing a dual-deep-learning pipeline: an infection-segmentation model built as a U-Net with a VGG16 encoder and a pneumonia-classification model that augments a VGG16-based encoder with a DenseNet121-inspired dense block (ChesXNet). Transfer learning with staged fine-tuning and Grad-CAM explainability underpin both models, enabling accurate localization of infection and robust classification across Normal, COVID-19, and non-COVID categories. Evaluations use the QaTa-COV19 infection-segmentation dataset and the COVID-19 Radiography Database, reporting high accuracy and strong localization (Dice and IoU metrics) along with a compact model footprint (~8M parameters) for the proposed classifier. The approach provides a practical, interpretable, and scalable solution for pneumonia and COVID-19 assessment with potential for real-world clinical deployment and telemedicine integration.

Abstract

Lung disease poses a substantial global health challenge, with pneumonia being a prevalent concern. This research focuses on leveraging deep learning techniques to detect and assess pneumonia, addressing two interconnected objectives. Initially, Convolutional Neural Network (CNN) models are introduced for pneumonia classification, emphasizing the necessity of comprehensive diagnostic assessments considering COVID-19. Subsequently, the study advocates for the utilization of deep learning-based segmentation to determine the severity of infection. This dual-pronged approach offers valuable insights for medical professionals, facilitating a more nuanced understanding and effective treatment of pneumonia. Integrating deep learning aims to elevate the accuracy and efficiency of pneumonia detection, thereby contributing to enhanced healthcare outcomes on a global scale.

Figures (14)

• Figure 1: Training of Infection Segmentation and Pneumonia Classification Models.
• Figure 2: U-Net for Infection Segmentation.
• Figure 3: Pneumonia Classification Model.
• Figure 4: Workflow for making predictions using the trained models.
• Figure 5: Training performance of the infection segmentation model.