  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.
