A new super resolution Faster R-CNN model based detection and classification of urine sediments
Authors:
- Derya Avci,
- Eser Sert,
- Esin Dogantekin,
- Ozal Yildirim,
- Ryszard Tadeusiewicz,
- Pawel Plawiak
Abstract
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.
- Record ID
- CUT9f6b4e3b62ca438c849f080074350248
- Publication categories
- ;
- Author
- Journal series
- Biocybernetics and Biomedical Engineering, ISSN 0208-5216
- Issue year
- 2023
- Vol
- 43
- No
- 1
- Pages
- 58-68
- Other elements of collation
- fot.; schem.; tab.; wykr.; Bibliografia (na s.) - 66-67; Bibliografia (liczba pozycji) - 43; Oznaczenie streszczenia - Abstr.; Data udostępnienia on-line - 2022-12-15; Numeracja w czasopiśmie - Vol. 43, Iss. 1
- Keywords in English
- Faster R-CNN, super resolution, urine sediment, DWT, Wiener filte
- ASJC Classification
- DOI
- DOI:10.1016/j.bbe.2022.12.001 Opening in a new tab
- URL
- https://www.sciencedirect.com/science/article/abs/pii/S0208521622001127 Opening in a new tab
- Language
- eng (en) English
- Score (nominal)
- 200
- Score source
- journalList
- Score
- Publication indicators
- Citation count
- 13
- Uniform Resource Identifier
- https://cris.pk.edu.pl/info/article/CUT9f6b4e3b62ca438c849f080074350248/
- URN
urn:pkr-prod:CUT9f6b4e3b62ca438c849f080074350248
* presented citation count is obtained through Internet information analysis, and it is close to the number calculated by the Publish or PerishOpening in a new tab system.