Pneumonia Detection Using Deep Learning

Abstract: As pneumonia still tops the list of the world’scausesofdeath,accuracyandtimelydiagnosisarestillvital. For pneumonia detection from large chest X-ray datasets, we presentacomputer-aidedmedicalimagediagnosissystemwith a three-layer detection Convolutional Neural Network (CNN) architecture. With hierarchical features through various layers, the proposed system enhances the diagnostic accuracy and classifies healthy and disease lungs according to the diagnosis appropriately. For enhanced usability, we integrate Stream lit with an interface that can handle real-time image uploading, modelprediction,andvisualizationofpneumoniadetectedareas. Our approach is more accurate than traditional deep learning architectures and thus real-clinical-practice deployable. Stream- lit based visualization makes the approach more end-user interactiveandreadable.ThissupportsAI-basedmedicaldiagnosisaswellasfacilitatespneumoniadetectionwithscalableandefficient modeling