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@article{171224,
        author = {Hardhik Sai Palivela and Aaditya Gawali and Annie Utthra},
        title = {Advancements in Pneumonia Detection Using U-Net},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {7},
        pages = {3673-3679},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=171224},
        abstract = {Pneumonia is an acute respiratory infection that continues to be a major cause of morbidity and mortality globally. Timely and precise diagnosis is crucial for effective treatment, but conventional approaches may face diagnostic limitations in accurate testing at a low-cost level. The objective of this study is to apply U-Net, a deep learning-based convolutional neural network (CNN) architecture, designed for biomedical image segmentation tasks, to automate pneumonia detection within chest X-ray medical images. We try to locate the infected areas in chest X-ray images using U-Net which uses a strong semantic segmentation architecture that can segment into pixels. We use a customized U-Net architecture, where we enhanced its features with attention techniques to emphasize the most notable parts of samples and enabling distinguishing between normal and pneumonia tissues more effectively.},
        keywords = {Medical Image Segmentation, U-Net, Attention Mechanisms, Deep Learning, Convolutional Neural Networks},
        month = {December},
        }