Application of Kronecker convolutions in deep learning technique for automated detection of kidney stones with coronal CT images
Article
Patro, Kiran Kumar, Allam, Jaya Prakash, Chakravarthy, Bala, Tadeusiewicz, Ryszard, Acharya, U Rajendra, Hammad,Mohamed, Yildirim, Ozal and Pławiak, Paweł. 2023. "Application of Kronecker convolutions in deep learning technique for automated detection of kidney stones with coronal CT images." Information Sciences. 640. https://doi.org/10.1016/j.ins.2023.119005
| Article Title | Application of Kronecker convolutions in deep learning technique for automated detection of kidney stones with coronal CT images |
|---|---|
| ERA Journal ID | 17908 |
| Article Category | Article |
| Authors | Patro, Kiran Kumar, Allam, Jaya Prakash, Chakravarthy, Bala, Tadeusiewicz, Ryszard, Acharya, U Rajendra, Hammad,Mohamed, Yildirim, Ozal and Pławiak, Paweł |
| Journal Title | Information Sciences |
| Journal Citation | 640 |
| Article Number | 119005 |
| Number of Pages | 16 |
| Year | 2023 |
| Publisher | Elsevier |
| Place of Publication | United Kingdom |
| ISSN | 0020-0255 |
| 1872-6291 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1016/j.ins.2023.119005 |
| Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S002002552300590X |
| 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. |
| Keywords | Coronal CT scan; Image embossing; Kidney stone; Kronecker convolution; Pre-processing |
| ANZSRC Field of Research 2020 | 400306. Computational physiology |
| Byline Affiliations | Aditya Institute of Technology and Management, India |
| National Institute of Technology Rourkela, India | |
| AGH University of Science and Technology, Poland | |
| School of Mathematics, Physics and Computing | |
| Prince Sultan University, Saudi Arabia | |
| Menoufia University, Egypt | |
| Firat University, Turkey | |
| Cracow University of Technology, Poland | |
| Polish Academy of Sciences, Poland |
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https://research.usq.edu.au/item/z1vz2/application-of-kronecker-convolutions-in-deep-learning-technique-for-automated-detection-of-kidney-stones-with-coronal-ct-images
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