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@article{160076,
        author = {Noor Fathima and Lakshmi M R and S.Tharunram and Owais Khan Hannan and Vibha S},
        title = {X-Ray Examination Using Deep Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {9},
        number = {12},
        pages = {1060-1064},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=160076},
        abstract = {Using the YOLOv7 object identification technique, we are constructing a deep learning model to detect bone fractures in X-ray pictures in this research. We are training the model on a big dataset of labelled X-ray pictures, which includes various sorts of bone fractures, and we are employing a variety of data sources.
Enhancement approaches are used to increase its performance and resilience. The main advantage of this study is that it has the potential to help healthcare personnel diagnose bone fractures, thereby lowering diagnosis time and improving patient outcomes. The model can help radiologists and other healthcare workers make better-informed decisions regarding patient treatment by properly diagnosing the location and kind of fracture inside an X- ray picture. Overall, this initiative has the potential to have a large impact.},
        keywords = {Deep Learning, YoloV7, Machine Learning},
        month = {},
        }