Akter, NasrinReza, Md TanzimAlam, Md. Ashraful2026-08-112026-08-112020-12-16N. Akter, M. T. Reza and M. A. Alam, "A Comparison Based Analysis on the Performance of Deep Neural Network Models in Terms of Classifying Pneumonia from Chest X-ray Images," 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE), Gold Coast, Australia, 2020, pp. 1-6, doi: 10.1109/CSDE50874.2020.9411560.97816654197412-s2.0-85105439692https://hdl.handle.net/10361/28916Pneumonia is one of those alarming diseases which causes a huge mortality rate among children and older people with 2 million deaths each year. People from the poor regions of Africa and Asia are mostly affected by pneumonia because of low medical monitoring in those regions. In recent times, a lot of computer aid based diagnostic systems have been developed in order to provide assistance in terms of detecting pneumonia. In this research work, we have proposed a convolutional neural network (CNN) based model comparison system for chest X-ray images to classify and detect pneumonia. A dataset containing 2,861 chest X-ray images of normal and pneumonia affected patients have been used to classify pneumonia from analyzing the lung images. We used 3 different neural network architectures: VGG16, Inception v3, ResNet50 in order to classify Pneumonia. After classification, we compared the result and we achieved a maximum of 95.0% accuracy, 94% precision, 96.40% sensitivity, 92.80% specificity from VGG16.6 Pagesen-USChest X-rayConvolutional neural networkComputer-aided diagnosisPneumoniaTransfer learningDiagnostic imaging.Image processing--Digital techniques.Pneumonia--Diagnosis.Conference Proceeding10.1109/CSDE50874.2020.9411560