01836nas a2200181   4500000000100000008004100001100001500042700001400057700002000071700001800091700002500109700001800134245010000152856007200252490000700324520130900331022001401640       2023                            d1 aDerya Avci1 aEser Sert1 aEsin Dogantekin1 aOzal Yildirim1 aRyszard Tadeusiewicz1 aPawel Plawiak00aA new super resolution Faster R-CNN model based detection and classification of urine sediments  uhttps://www.sciencedirect.com/science/article/pii/S02085216220011270 v433 a<p>The diagnosis of urinary tract infections and kidney diseases using urine microscopy images has gained significant attention of medical community in recent years. These images are usually created by physicians’ own rule of thumb manually. However, this manual urine sediment analysis is usually labor-intensive and time-consuming. In addition, even when physicians carefully examine an image, an erroneous cell recognition may occur due to some optical illusions. In order to achieve cell recognition in low-resolution urine microscopy images with a higher level of accuracy, a new super resolution Faster Region-based Convolutional Neural Network (Faster R-CNN) method is proposed. It aims to increase resolution in low-resolution urine microscopy images using self-similarity based single image super resolution which was used during the pre-processing. De-noising based Wiener filter and Discrete Wavelet Transform (DWT) are used to de-noise high resolution images, respectively, to increase the level of accuracy for image recognition. Finally, for the feature extraction and classification stages, AlexNet, VGFG16 and VGG19 based Faster R-CNN models are used for the recognition and detection of multi-class cells. The model yielded accuracy rates are 98.6%, 96.4% and 96.2% respectively.</p>
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