Prithvi Sairaj Krishnan, USA

Pneumonia is a major respiratory infection causing significant global morbidity and mortality, especially in developing nations with inadequate medical infrastructure. Early diagnosis through chest X-ray imaging is crucial but challenging. This study developed an automated computer-aided diagnosis system using deep learning to detect pneumonia from chest X-rays. An ensemble of three pre-trained convolutional neural network models (GoogLeNet, ResNet-18, DenseNet-121) was employed, with a novel weighted average ensemble technique based on evaluation metric scores. Evaluated on two public pneumonia X-ray datasets using five-fold cross-validation, the approach achieved high accuracy (98.2%, 86.7%) and sensitivity (98.19%, 86.62%), outperforming state-of-the-art methods. With pneumonia-causing over 2.5 million annual deaths worldwide, this accurate automated model can assist radiologists in timely diagnosis, especially in resource-limited settings. Its integration into clinical decision support systems has the potential to improve pneumonia management and outcomes significantly.

Convolutional Neural Networks, Pneumonia, X-Rays, Model, Machine Learning