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Pneumonia is one of the most acute respiratory diseases having remarkablyhigh prevalence and mortality rate. Chest X-ray (CXR) has been widely utilizedfor the diagnosis of this disease owing to its availability, diagnostic speedand accuracy. However, even for an expert radiologist, it is quite challengingto readily determine pneumonia opacity by examining CXRs. Therefore, this studyhas been structured to automate the pneumonia detection process by introducinga robust deep learning framework. The proposed network comprises of ProcessConvolution (Pro_Conv) blocks for feature accumulation inside Dual Feedback(DF) blocks to propagate the feature maps towards a viable detection.Experimental analysis showcase: (1) the proposed network proficientlydistinguishes between normal and pneumonia opacity containing CXRs with themean accuracy, sensitivity and specificity of 97.78%, 98.84% and 95.04%,respectively; (2) the network is constructed with significantly low parametersthan the traditional ImageNets to reduce memory consumption for deployment inmemory constrained mobile platforms; (3) the trade-off between accuracy andnumber of parameters of the model outperforms the considered classical networksby a remarkable margin; and (4) the false-negatives are lower than thefalse-positives (both of which are low in count) which prove the model'slow-fatality prediction. Hence, the proposed network can be deployed for arapid screening of pneumonia and can act as a great assistive tool for theradiologists in the diagnosis process.

A Robust CNN Framework with Dual Feedback Feature Accumulation for Detecting Pneumonia Opacity from Chest X-ray Images