InfLocNet: Enhanced Lung Infection Localization and Disease Detection from Chest X-Ray Images Using Lightweight Deep Learning

TL;DR

This paper introduces a lightweight deep learning model that improves lung infection localization and disease detection from chest X-ray images, combining segmentation and classification for accurate, efficient, and real-time clinical diagnosis.

Contribution

The novel model integrates enhanced UNet++ architecture with transfer learning and dual segmentation-classification tasks, achieving high accuracy with reduced computational complexity.

Findings

Achieved 93.59% IoU and 97.61% DSC in lung segmentation.

Attained 97.67% IoU and 87.61% DSC in infection localization.

Demonstrated 93.86% accuracy and 89.55% sensitivity in disease detection.

Abstract

In recent years, the integration of deep learning techniques into medical imaging has revolutionized the diagnosis and treatment of lung diseases, particularly in the context of COVID-19 and pneumonia. This paper presents a novel, lightweight deep learning based segmentation-classification network designed to enhance the detection and localization of lung infections using chest X-ray images. By leveraging the power of transfer learning with pre-trained VGG-16 weights, our model achieves robust performance even with limited training data. The architecture incorporates refined skip connections within the UNet++ framework, reducing semantic gaps and improving precision in segmentation tasks. Additionally, a classification module is integrated at the end of the encoder block, enabling simultaneous classification and segmentation. This dual functionality enhances the model's versatility, providing comprehensive diagnostic insights while optimizing computational efficiency. Experimental results demonstrate that our proposed lightweight network outperforms existing methods in terms of accuracy and computational requirements, making it a viable solution for real-time and resource constrained medical imaging applications. Furthermore, the streamlined design facilitates easier hyperparameter tuning and deployment on edge devices. This work underscores the potential of advanced deep learning architectures in improving clinical outcomes through precise and efficient medical image analysis. Our model achieved remarkable results with an Intersection over Union (IoU) of 93.59% and a Dice Similarity Coefficient (DSC) of 97.61% in lung area segmentation, and an IoU of 97.67% and a DSC of 87.61% for infection region localization. Additionally, it demonstrated high accuracy of 93.86% and sensitivity of 89.55% in detecting chest diseases, highlighting its efficacy and reliability.