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@article{153693,
        author = {S.Ashlin lifty and R.Deepa},
        title = {CNN BASED IMAGE QUALITY IMPROVEMENT IN HAND HELD ULTRASOUND DEVICES},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {8},
        number = {8},
        pages = {344-350},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=153693},
        abstract = {Ultrasound technologies have grown popular in the medical field because they are more accurate, however the image quality of hand-held ultrasound devices is comparably low. The suggested method uses Convolutional Neural Networks to improve the image standard in handheld devices to high visuals. The suggested Convolutional   Neural   Networks   to  improve the Image  standard  in  handheld   devices,  leading to High visuals. Through histogram equalization, the median filter is used to reduce undesired disturbance and to keep the details while maintaining a high dynamic range .Histogram equalisation approach is used to alter the dynamic value using its histogram.. It spreads out the most frequent pixel intensity values or stretches out the image's intensity range to improve the image contrast. Contrary to what its name suggests, unsharp masking is used to sharpen an image. Sharpening is important when post-processing most digital photos since it helps to emphasis detail. To accomplish higher resolution, a  Convolutional Neural Network is often used. CNN was created primarily to handle pixel data. It's a hierarchical model that builds a network, similar to a funnel, and then outputs a fully-connected layer where all the neurons are connected to one another and the output is processed. By employing CNN, it can provide more accurate training with high accuracy and produce a high-quality reconstruction image with fine details, structure, and speckle.},
        keywords = {Portable ultrasound device, CNN, Image quality improvement, Median filter, Histogram Equalisation, Unsharp masking},
        month = {},
        }