Application of Kronecker convolutions in deep learning technique for automated detection of kidney stones with coronal CT images
Authors:
- Kiran Kumar Patro,
- Allam Jaya Prakash,
- Bala Chakravarthi Neelapu,
- Ryszard Tadeusiewicz,
- U Rajendra Acharya,
- Mohamed Hammad,
- Ozal Yildirim,
- Paweł Pławiak
Abstract
Kidney stone disease is a serious public health concern that is getting worse with changes in diet, obesity, medical conditions, certain supplements etc. A kidney stone also called a renal calculus, is a hard buildup of urine minerals that form in the kidneys. Computed tomography (CT) is one of the imaging models used to identify kidney stones by clinical experts. Due to the low resolution of these images, sometimes detecting kidney stones is tedious with the naked eye, which may lead to false alarms. In this work, a computer-based diagnosis system with a deep learning technique has been developed as a practical solution to aid clinicians in their diagnosis. The traditional convolutional neural network (CNN)-based deep learning technology can detect stones in the kidney. Still, it suffers from the performance and standard implementation of the convolution operations in convolution layers. A Kronecker product-based convolution technique is incorporated in the proposed deep learning architecture to reduce the redundancy in feature maps without convolution overlapping. Our proposed method helps to make the network more effective by extracting abstract and in-depth features from the input images. The publicly available GitHub kidney stone CT scans are utilized to develop the proposed architecture. Our automated model detected kidney stones with an accuracy of 98.56% utilizing CT images. Our system is more effective than the most recent and cutting-edge techniques developed for identifying kidney stones of any size, including the smallest ones.
- Record ID
- CUTc783d258819445c09e1572a1e94d443d
- Publication categories
- ;
- Author
- Journal series
- Information Sciences, ISSN 0020-0255, e-ISSN 1872-6291
- Issue year
- 2023
- Vol
- 640
- Pages
- [1-16]
- Article number
- 119005
- Other elements of collation
- rys.; schem.; tab.; wykr.; Bibliografia (na s.) - 15-16; Bibliografia (liczba pozycji) - 50; Oznaczenie streszczenia - Abstr.; Data udostępnienia on-line - 2023-04-25; Numeracja w czasopiśmie - Vol. 640
- Keywords in English
- coronal CT scan, image embossing, kidney stone, Kronecker convolution, pre-processing
- ASJC Classification
- ; ; ; ; ;
- DOI
- DOI:10.1016/j.ins.2023.119005 Opening in a new tab
- URL
- https://www.sciencedirect.com/science/article/pii/S002002552300590X Opening in a new tab
- Language
- eng (en) English
- License
- Score (nominal)
- 200
- Score source
- journalList
- Score
- Publication indicators
- Citation count
- 20
- Uniform Resource Identifier
- https://cris.pk.edu.pl/info/article/CUTc783d258819445c09e1572a1e94d443d/
- URN
urn:pkr-prod:CUTc783d258819445c09e1572a1e94d443d
* 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.