Lung Diseases Image Segmentation using Faster R-CNNs

TL;DR

This paper presents an improved neural network approach using Faster R-CNNs for lung disease detection in chest X-ray images, emphasizing enhanced data extraction and optimized proposal generation.

Contribution

Introduces a low-density neural network with a feature pyramid and Soft Non-Maximal Suppression for better lung disease image segmentation.

Findings

High accuracy in lung disease detection

Effective reduction of information loss

Improved regional proposal quality

Abstract

Lung diseases are a leading cause of child mortality in the developing world, with India accounting for approximately half of global pneumonia deaths (370,000) in 2016. Timely diagnosis is crucial for reducing mortality rates. This paper introduces a low-density neural network structure to mitigate topological challenges in deep networks. The network incorporates parameters into a feature pyramid, enhancing data extraction and minimizing information loss. Soft Non-Maximal Suppression optimizes regional proposals generated by the Region Proposal Network. The study evaluates the model on chest X-ray images, computing a confusion matrix to determine accuracy, precision, sensitivity, and specificity. We analyze loss functions, highlighting their trends during training. The regional proposal loss and classification loss assess model performance during training and classification phases. This paper analysis lung disease detection and neural network structures.