Automatic Kidney Stone Detection using Low-cost CNN with Coronal CT Images
Resumo
A fast diagnosis of kidney stones is crucial to start the correct treatment, minimizing the risks of urinary complications. Machine learning approaches are valuable for an automatic diagnosis system for kidney stones from computer tomography (CT) exams. Recently, related studies achieved high accuracy in detecting kidney stones using deep-learning neural networks. However, their approaches were highly complex and time-consuming. This paper proposes a method for automatically detecting kidney stones on CT images using low-complexity deep-learning techniques. We compared three models based on Convolutional Neural Networks (CNN): 3-layered CNN (Conv3), 4-layered CNN (Conv4), and MobileNetV1. They were applied to kidney stone detection using 1799 CT images divided into 80% for training, 10% for validation, and 10% for testing. The proposed model Conv4 obtained the best performance, achieving a test accuracy of 97.2% and an F1-score of 97.6%, with a training time of 140 seconds.
Palavras-chave:
Kidney Stone Detection, Deep Learning, Convolutional Neural Network, MobileNetV1, Computed Tomography
Referências
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W. Brisbane, M. R. Bailey, and M. D. Sorensen, “An overview of kidney stone imaging techniques,” Nature Reviews Urology, vol. 13, no. 11, pp. 654–662, 2016.
A. A. Shokeir, “Renal colic: new concepts related to pathophysiology, diagnosis and treatment,” Current opinion in urology, vol. 12, no. 4, pp. 263–269, 2002.
J. P. Ingimarsson, A. E. Krambeck, and V. M. Pais, “Diagnosis and management of nephrolithiasis,” Surgical Clinics, vol. 96, no. 3, pp. 517–532, 2016.
N. Rasulova, A. Aminova, and F. Ismailova, “Improvement of early diagnosis and prevention measures of kidney stone diseases among the population,” Science and innovation, vol. 2, no. D3, pp. 61–66, 2023.
Y. Andrabi, M. Patino, C. J. Das, B. Eisner, D. V. Sahani, and A. Kambadakone, “Advances in ct imaging for urolithiasis,” Indian journal of urology: IJU: journal of the Urological Society of India, vol. 31, no. 3, p. 185, 2015.
H.-P. Chan, L. M. Hadjiiski, and R. K. Samala, “Computer-aided diagnosis in the era of deep learning,” Medical physics, vol. 47, no. 5, pp. e218–e227, 2020.
M. H. Hesamian, W. Jia, X. He, and P. Kennedy, “Deep learning techniques for medical image segmentation: achievements and challenges,” Journal of digital imaging, vol. 32, pp. 582–596, 2019.
B. Z. Hameed, A. V. S. Dhavileswarapu, S. Z. Raza, H. Karimi, H. S. Khanuja, D. K. Shetty, S. Ibrahim, M. J. Shah, N. Naik, R. Paul et al., “Artificial intelligence and its impact on urological diseases and management: A comprehensive review of the literature,” Journal of Clinical Medicine, vol. 10, no. 9, p. 1864, 2021.
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, p. 441, 2022.
R. Suarez-Ibarrola, S. Hein, G. Reis, C. Gratzke, and A. Miernik, “Current and future applications of machine and deep learning in urology: a review of the literature on urolithiasis, renal cell carcinoma, and bladder and prostate cancer,” World journal of urology, vol. 38, pp. 2329–2347, 2020.
M. Akshaya, R. Nithushaa, N. S. M. Raja, and S. Padmapriya, “Kidney stone detection using neural networks,” in 2020 International Conference on System, Computation, Automation and Networking (ICSCAN). IEEE, 2020, pp. 1–4.
S. G. Mallat, “A theory for multiresolution signal decomposition: the wavelet representation,” IEEE transactions on pattern analysis and machine intelligence, vol. 11, no. 7, pp. 674–693, 1989.
D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning representations by back-propagating errors,” nature, vol. 323, no. 6088, pp. 533–536, 1986.
J. R. S. Antony, U. S. RonyJoseph, P. Vijayaragavan, and A. Jerrinsimla, “Refine observation of kidney stones using neural network,” 2020.
A. Soni and A. Rai, “Kidney stone recognition and extraction using directional emboss & svm from computed tomography images,” in 2020 Third International Conference on Multimedia Processing, Communication & Information Technology (MPCIT). IEEE, 2020, pp. 57–62.
C. Cortes and V. Vapnik, “Support-vector networks,” Machine learning, vol. 20, pp. 273–297, 1995.
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, 2018.
O. Joseph and W. O. Apena, “Development of segmentation and classification algorithms for computed tomography images of human kidney stone,” Journal of Electronic Research and Application, vol. 5, no. 5, pp. 1–10, 2021.
A. Caglayan, M. O. Horsanali, K. Kocadurdu, E. Ismailoglu, and S. Guneyli, “Deep learning model-assisted detection of kidney stones on computed tomography,” International braz j urol, vol. 48, pp. 830–839, 2022.
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, p. 104569, 2021.
B. Jou and S.-F. Chang, “Deep cross residual learning for multitask visual recognition,” in Proceedings of the 24th ACM international conference on Multimedia, 2016, pp. 998–1007.
M. Baygin, O. Yaman, P. D. Barua, S. Dogan, T. Tuncer, and U. R. Acharya, “Exemplar darknet19 feature generation technique for automated kidney stone detection with coronal ct images,” Artificial Intelligence in Medicine, vol. 127, p. 102274, 2022.
J. Redmon and A. Farhadi, “Yolo9000: better, faster, stronger,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 7263–7271.
T. Tuncer, S. Dogan, F. Özyurt, S. B. Belhaouari, and H. Bensmail, “Novel multi center and threshold ternary pattern based method for disease detection method using voice,” IEEE Access, vol. 8, pp. 84 532–84 540, 2020.
E. Fix, Discriminatory analysis: nonparametric discrimination, consistency properties. USAF school of Aviation Medicine, 1985, vol. 1.
K. K. Patro, J. P. Allam, B. C. Neelapu, R. Tadeusiewicz, U. R. Acharya, M. Hammad, O. Yildirim, and P. Pławiak, “Application of kronecker convolutions in deep learning technique for automated detection of kidney stones with coronal ct images,” Information Sciences, vol. 640, p. 119005, 2023.
T. Wu, S. Tang, R. Zhang, J. Cao, and J. Li, “Tree-structured kronecker convolutional network for semantic segmentation,” in 2019 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2019, pp. 940–945.
D. B. Adarkar, A. Lokapur, J. Porwal, and P. Mali, “Chronic kidney disease prediction,” International Journal for Research in Applied Science and Engineering Technology, 2023. [Online]. Available: https://api.semanticscholar.org/CorpusID:258448985
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE, vol. 86, no. 11, pp. 2278–2324, 1998.
A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam, “Mobilenets: Efficient convolutional neural networks for mobile vision applications,” arXiv preprint arXiv:1704.04861, 2017.
W. Brisbane, M. R. Bailey, and M. D. Sorensen, “An overview of kidney stone imaging techniques,” Nature Reviews Urology, vol. 13, no. 11, pp. 654–662, 2016.
Publicado
13/11/2023
Como Citar
BOUZON, Murillo Freitas; OLIVEIRA, Samuel Patrício de; FUGITA, Oscar Eduardo Hidetoshi; RODRIGUES, Paulo Sergio Silva.
Automatic Kidney Stone Detection using Low-cost CNN with Coronal CT Images. In: WORKSHOP DE VISÃO COMPUTACIONAL (WVC), 18. , 2023, São Bernardo do Campo/SP.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 24-29.
DOI: https://doi.org/10.5753/wvc.2023.27527.
