Lung Diseases Image Segmentation using Faster R-CNNs
It addresses timely diagnosis of lung diseases like pneumonia in children, particularly in developing regions such as India, but appears incremental in method.
This paper tackled lung disease detection from chest X-ray images using a modified Faster R-CNN with a low-density neural network structure and Soft Non-Maximal Suppression, achieving results evaluated through accuracy, precision, sensitivity, and specificity metrics.
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.