@article{bibcite_15905,
  author = {Derya Avci and Eser Sert and Esin Dogantekin and Ozal Yildirim and Ryszard Tadeusiewicz and Pawel Plawiak},
  title = {A new super resolution Faster R-CNN model based detection and classification of urine sediments},
  abstract = {<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{\textquoteright} 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>
},
  year = {2023},
  journal = {Biocybernetics and Biomedical Engineering},
  volume = {43},
  issn = {0208-5216},
  url = {https://www.sciencedirect.com/science/article/pii/S0208521622001127},
  doi = {https://doi.org/10.1016/j.bbe.2022.12.001},
  language = {eng},
}