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Y. Kazemi and S. A. Mirroshandel, “A novel method for predicting kidney stone type using ensemble learning,” Artificial Intelligence in Medicine, vol. 84, pp. 117–126, Jan. 2018. https://doi.org/10.1016/j.artmed.2017.12.001Search in Google Scholar
A. Parakh, H. Lee, J. H. Lee, B. H. Eisner, D. V. Sahani, and S. Do, “Urinary stone detection on CT images using deep convolutional neural networks: evaluation of model performance and generalization,” Radiology: Artificial Intelligence, vol. 1, no.4, Jul. 2019, Art. no. e180066. https://doi.org/10.1148/ryai.2019180066Search in Google Scholar
F. Ma, T. Sun, L. Liu, and H. Jing, “Detection and diagnosis of chronic kidney disease using deep learning-based heterogeneous modified artificial neural network,” Future Generation Computer Systems, vol. 111, pp. 17–26, Oct. 2020. https://doi.org/10.1016/j.future.2020.04.036Search in Google Scholar
K. Viswanath, B. Anilkumar, and R. Gunasundari, “Design of deep learning reaction-diffusion level set segmentation approach for health care related to automatic kidney stone detection analysis,” Multimedia Tools and Applications, vol. 81, no. 29, pp. 41807–41849, Jul. 2022. https://doi.org/10.1007/s11042-021-11263-7Search in Google Scholar
D. C. Elton, E. B. Turkbey, P. J. Pickhardt, and R. M. Summers, “A deep learning system for automated kidney stone detection and volumetric segmentation on noncontrast CT scans,” Medical Physics, vol. 49, no. 4, pp. 2545–2554, Feb. 2022. https://doi.org/10.1002/mp.15518Search in Google Scholar
J. D. Arias-Londoño, J. A. Gómez-García, L. Moro-Velázquez, and J. I. Godino-Llorente, “Artificial intelligence applied to chest X-ray images for the automatic detection of COVID-19. A thoughtful evaluation approach,” IEEE Access, vol. 8, pp. 226811–226827, Dec. 2020. https://doi.org/10.1109/ACCESS.2020.3044858Search in Google Scholar
B. Manoj, N. Mohan, S. S. Kumar, and K. P. Soman, “Automated detection of kidney stone using deep learning models,” in 2022 2nd international conference on intelligent technologies (CONIT), Hubli, India, Jun. 2022, pp. 1–5. https://doi.org/10.1109/CONIT55038.2022.9847894Search in Google Scholar
K. M. Black, H. Law, A. Aldoukhi, J. Deng, and K. R. Ghani, “Deep learning computer vision algorithm for detecting kidney stone composition,” BJU International, vol. 125, no. 6, pp. 920–924, Jun. 2020. https://doi.org/10.1111/bju.15035Search in Google Scholar
K. Yildirim, P. G. Bozdag, M. Talo, O. Yildirim, M. Karabatak, and U. R. Acharya, “Deep learning model for automated kidney stone detection using coronal CT images,” Computers in Biology and Medicine, vol. 135, Aug. 2021, Art. no. 104569. https://doi.org/10.1016/j.compbiomed.2021.104569Search in Google Scholar
U. S. Kim, H. S. Kwon, W. Yang, W. Lee, C. Choi, J. K. Kim, S. H. Lee, D. Rim, and J. H. Han, “Prediction of the composition of urinary stones using deep learning,” Investigative and Clinical Urology, vol. 63, no. 4, May 2022, Art. no. 441. https://doi.org/10.4111/icu.20220062Search in Google Scholar
S. Azizi et al., “Big self-supervised models advance medical image classification,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, Montreal, QC, Canada, Oct. 2021, pp. 3478–3488. https://doi.org/10.1109/ICCV48922.2021.00346Search in Google Scholar
Y. Celik, M. Talo, O. Yildirim, M. Karabatak, and U. R. Acharya, “Automated invasive ductal carcinoma detection based using deep transfer learning with whole-slide images,” Pattern Recognition Letters, vol. 133, pp. 232–239, May 2020. https://doi.org/10.1016/j.patrec.2020.03.011Search in Google Scholar
A. Chewcharat and G. Curhan, “Trends in the prevalence of kidney stones in the United States from 2007 to 2016,” Urolithiasis, vol. 49, no. 1, pp. 27–39, Sep. 2021. https://doi.org/10.1007/s00240-020-01210-wSearch in Google Scholar
G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks, ” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, Jul. 2017, pp. 4700–4708. https://doi.org/10.1109/CVPR.2017.243Search in Google Scholar
S. S. Kumar, M. A. Kumar, and K. P. Soman, “Sentiment analysis of tweets in Malayalam using long short-term memory units and convolutional neural nets,” in Mining Intelligence and Knowledge Exploration: 5th International Conference, MIKE 2017, Hyderabad, India, Dec. 2017, pp. 320–334. https://doi.org/10.1007/978-3-319-71928-3_31Search in Google Scholar
N. Mohan, K. P. Soman, and R. Vinayakumar, “Deep power: Deep learning architectures for power quality disturbances classification,” in 2017 International Conference on Technological Advancements in Power and Energy (TAP Energy), Kollam, India, Dec. 2017, pp. 1–6. https://doi.org/10.1109/TAPENERGY.2017.8397249Search in Google Scholar
M. Längkvist, J. Jendeberg, P. Thunberg, A. Loutfi, and M. Lidén, “Computer aided detection of ureteral stones in thin slice computed tomography volumes using convolutional neural networks,” Computers in Biology and Medicine, vol. 97, pp. 153–160, Jun. 2018. https://doi.org/10.1016/j.compbiomed.2018.04.021Search in Google Scholar