Karim, Dewan ZiaulPriya, Afsana RahmanSarkar, KashmiraKhan, Tania RahmanTurna, Sohani FatehinMubashshira, Sadia2023-12-202023-12-2020232023-05ID 19301181ID 19101139ID 19101513ID 19301132ID 19101664http://hdl.handle.net/10361/22011Cataloged from PDF version of thesis.Includes bibliographical references (pages 36-37).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023.A bacterial infection is the cause of the lung condition known as pneumonia. An essential component of a successful treatment procedure is early diagnosis. Without early diagnosis, pneumonia can be severe or even can cause death. Viewing X-ray images is one of the ways to detect pneumonia. For accurate viewing or reading of X-ray images, a computer-based algorithm is preferable over reading X-ray images manually. In this study, a pneumonia detection system is created using grounded feature extraction from convolutional neural networks (CNN). To predict the occurrence of pneumonia, different classification algorithm models are used. For classi-fication, customized CNN models and various pre-trained models such as VGG-16, Inceptionv3, ResNet50, and VGG-19 are applied to the x-ray image dataset. After implementing all these models we obtained our best accuracy from the Customized CNN model which is 90.43% and the best f1-score from Customized CNN, ResNet50, and VGG-19, the score is 0.87.37 pagesenBrac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.X-ray imagesComputer-based algorithmCustomized CNN modelPre-trained modelsVGG-16Inceptionv3ResNet50VGG-19AccuracyF1-ScoreMachine learningNeural networks (Computer science)Thesis