Alam, Md. AshrafulHassan, Sanzana MahrukhKhan, Md. AnikHossine, Md. AbidLamia, Mayesha ZamanSarkar, Pritom Kumar2025-06-252025-06-2520252025-02ID 21101237ID 24241273ID 20301392ID 21301686ID 20301372http://hdl.handle.net/10361/26308Cataloged from PDF version of thesis.Includes bibliographical references (pages 58-60).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2025.We propose and demonstrate an efficient deep-learning approach to classify various diseases using chest x-ray images. The proposed system comprises several steps: image acquisition, preprocessing, and classification of various diseases. The datasets include X-ray images of various diseases such as pneumonia, COVID-19, lung opacity, and normal chest images. Raw X-ray images and the dataset from Kaggle is preprocessed using image resizing and augmentation. Finally, a network-based deep learning model is applied to classify the disease. Different CNN architectures: ResNet50, ResNet101, EfficientNet, DenseNet121, and AlexNet are investigated for the classification, and the best-performing architecture is used in the model, to design a custom-made model named X Net. By incorporating certain layers from both ResNet101 and DenseNet121. The ResNet101 and DenseNet121 models gave 94% and 92% accuracy, respectively, where the rest of the models gave lower accuracy than them. Our proposed model achieves a higher accuracy of upto 96.5%.60 pagesenBRAC University theses reports 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.CNNEfficientNetAlexNetResNet50ResNet101DenseNet121Attention mechanismDeep learning algorithmsCognitive learning theoryDiagnostic imaging--Digital techniques.Thesis