Citation:

Buvaneswari R, Vinoth R, 2022. "Kidney Stone Detection Using Hybrid Butterfly Net and Inception net Model"ESP Journal of Engineering & Technology Advancements  2(4): 45-50.

Abstract:

Kidney stones are the to be expected complaint global, incurring numerous people to admit to trauma centers with outrageous agony. different imaging procedures are utilized for the guess of kidney stone ailment. experts are needed for the interpretation and complete anticipation of these photographs. pc-supported examination structures are the reasonable strategies that might be utilized as helper devices to help the clinicians of their conclusion. in this examine, a computerized recognition of kidney stone (having stone/presently not) utilizing ultrasound pics is proposed with profound learning (DL) procedure which has as of late made enormous advancement in the space of manufactured knowledge. This work proposes HybriNet Butterfly-web, by means of consolidating InceptionNet and ButterflyNet for precise division. A low-intricacy CNN with organized and meager cross channel associations, all in all with an Inception layers procedure for kidney stone identification. Joining Butterfly-net with a half and half layers, a huge greatness of issues is ended up being very much approximated with network intricacy relying upon the powerful recurrence transmission capacity as opposed to the enter aspect the proposed variant carried out utilizing python programming and as looked at in expressions of exactness, F score and responsiveness values

References:

[1] Yuan, Q., Zhang, H., Deng, T., Tang, S., Yuan, X., Tang, W., … Xiao, X. (2020). Role of Artificial Intelligence in Kidney Disease. International Journal of Medical Sciences, 17(7), 970–984.
[2] Cui, X., Che, F., Wang, N., Liu, X., Zhu, Y., Zhao, Y., … Zhang, G. (2019). Preoperative Prediction of Infection Stones Using Radiomics Features From Computed Tomography. IEEE Access, 7, 122675–122683.
[3] Kazemi, Y., & Mirroshandel, S. A. (2018). A novel method for predicting kidney stone type using ensemble learning. Artificial Intelligence in Medicine, 84, 117–126.
[4] Yin, S., Peng, Q., Li, H., Zhang, Z., You, X., Fischer, K., … Fan, Y. (2019). Automatic kidney segmentation in ultrasound images using subsequent boundary distance regression and pixelwise classification networks. Medical Image Analysis, 101602.
[5] Yildirim, K., Bozdag, P. G., Talo, M., Yildirim, O., Karabatak, M., & Acharya, U. R. (2021). Deep learning model for automated kidney stone detection using coronal CT images. Computers in Biology and Medicine, 135, 104569.
[6] Sudharson, S., & Kokil, P. (2020). An ensemble of deep neural networks for kidney ultrasound image classification. Computer Methods and Programs in Biomedicine, 105709.
[7] Yun-Te Liao,Chien-Hung Lee and Kuo-Su Chen,,Data Augmentation Based on Generative Adversarial Networks to Improve Stage Classification of Chronic Kidney Disease, Recent Advances in Deep Learning for Image Analysis, 2022, 12(1), 352
[8] M. Ranjitha, "Extraction and dimensionality reduction of features for Renal Calculi detection and artifact differentiation from segmented ultrasound kidney images," 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom), 2016, pp. 3087-3092.
[9] P. Vaish, R. Bharath, P. Rajalakshmi and U. B. Desai, "Smartphone based automatic abnormality detection of kidney in ultrasound images," 2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom), 2016, pp. 1-6, doi: 10.1109/HealthCom.2016.7749492.
[10] S. Hu et al., "Towards quantification of kidney stones using X-ray dark-field tomography," 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), 2017, pp. 1112-1115, doi: 10.1109/ISBI.2017.7950711.
[11] P. T. Akkasaligar, S. Biradar and V. Kumbar, "Kidney stone detection in computed tomography images," 2017 International Conference On Smart Technologies For Smart Nation (SmartTechCon), 2017, pp. 353-356, doi: 10.1109/SmartTechCon.2017.8358395.
[12] N. Thein, K. Hamamoto, H. A. Nugroho and T. B. Adji, "A comparison of three preprocessing techniques for kidney stone segmentation in CT scan images," 2018 11th Biomedical Engineering International Conference (BMEiCON), 2018, pp. 1-5, doi: 10.1109/BMEiCON.2018.8609996.
[13] N. Thein, H. A. Nugroho, T. B. Adji and K. Hamamoto, "An image preprocessing method for kidney stone segmentation in CT scan images," 2018 International Conference on Computer Engineering, Network and Intelligent Multimedia (CENIM), 2018, pp. 147-150, doi: 10.1109/CENIM.2018.8710933.
[14] T. Shah and S. Kadge, "Analysis and Identification of Renal Calculi in Computed Tomography Images," 2019 International Conference on Nascent Technologies in Engineering (ICNTE), 2019, pp. 1-4, doi: 10.1109/ICNTE44896.2019.8945877.
[15] M. K. Shahina and H. S. Mahesh, "Renal Stone Detection and Analysis by Contour Based Algorithm," 2019 International Conference on Recent Advances in Energy-efficient Computing and Communication (ICRAECC), 2019, pp. 1-5, doi: 10.1109/ICRAECC43874.2019.8994967.
[16] M. Akshaya, R. Nithushaa, N. S. M. Raja and S. Padmapriya, "Kidney Stone Detection Using Neural Networks," 2020 International Conference on System, Computation, Automation and Networking (ICSCAN), 2020, pp. 1-4, doi: 10.1109/ICSCAN49426.2020.9262335.
[17] A. Soni and A. Rai, "Kidney Stone Recognition and Extraction using Directional Emboss & SVM from Computed Tomography Images," 2020 Third International Conference on Multimedia Processing, Communication & Information Technology (MPCIT), 2020, pp. 57-62, doi: 10.1109/MPCIT51588.2020.9350388.
[18] L. Y. Myint, S. S. Maung and K. T. Zar, "Removal of Unwanted Object in 3D CT Kidney Stone Images and 3D Visualization," 2020 24th International Computer Science and Engineering Conference (ICSEC), 2020, pp. 1 -5, doi: 10.1109/ICSEC51790.2020.9375155.
[19] H. Dave, V. Patel, J. N. Mehta, S. Degadwala and D. Vyas, "Regional Kidney Stone Detection and Classification In Ultrasound Images," 2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA), 2021, pp. 1108 -1112, doi: 10.1109/ICIRCA51532.2021.9545031.
[20] S. M B and A. M R, "Kidney Stone Detection Using Digital Image Processing Techniques," 2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA), 2021, pp. 556 -561, doi: 10.1109/ICIRCA51532.2021.9544610.
[21] S. Rajput, A. Singh and R. Gupta, "Automated Kidney Stone Detection Using Image Processing Techniques," 2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), 2021, pp. 1 -5, doi: 10.1109/ICRITO51393.2021.9596175.
[22] Doctor, A., B. Vondenbusch, and J. Kozak. "Bone segmentation applying rigid bone position and triple shadow check method based on RF data." Acta of Bioengineering and Biomechanics, 13.2 (2011): 3-11.
[23] Bhat, V. Gojanur, and R. Hegde. 2015. 4G protocol and architecture for BYOD over Cloud Computing. In Communications and Signal Processing (ICCSP), 2015 International Conference on. 0308-0313. Google Scholar.
[24] Chanthati, Sasibhushan Rao. (2022). A Centralized Approach To Reducing Burnouts In The It Industry Using Work Pattern Monitoring Using Artificial Intelligenc. International Journal on Soft Computing Artificial Intelligence and Applications. Sasibhushan Rao Chanthati. Volume-10, Issue-1, PP 64-69.