Kidney Diseases Classification using Hybrid Transfer-Learning DenseNet201-Based and Random Forest Classifier
https://doi.org/10.24017/Science.2022.2.11
Abstract views: 952 / PDF downloads: 423Abstract
There are several disease kinds in global populations that may be related to human lifestyles, social, genetic, economic, and other factors related to the nature of the country they live in. Most of the recent studies have focused on investigating prevalent diseases that spread in the population in order to minimize mortality risks, choose the best method for treatment, and improve community healthcare. Kidney disease is one of the most widespread health problems in modern society. This study focuses on kidney stones, cysts, and tumors, the three most common types of renal illness, using a dataset of 12,446 CT urogram and whole abdomen images, aiming to move toward an AI-based kidney disease diagnosis system while contributing to the wider field of artificial intelligence research. In this study, a hybrid technique is used by utilizing both pre-train models for feature extraction and classification using machine learning algorithms for the task of kidney disease image diagnosis. The pre-trained model used in this study is the Densenet-201 model. As well as using Random Forest for classification, the Densenet-201-Random-Forest approach has outperformed many of the previous models used in other studies, having an accuracy rate of 99.719 percent.
Keywords:
kidney Disease, AI-Based System, Machine Learning, Pre-Trained Model, Feature Extraction, Classification
References
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https://doi.org/10.1016/S0140-6736(18)31694-5
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[5] AJ. Brownstein, S. Mahmood, A. Saeyeldin, CV. Mejia, MA. Zafar, Y. Li, JA. Rizzo, NK. Dahl, Y. Erben, BA. Ziganshin,JA. Elefteriades, "Simple renal cysts and bovine aortic arch: markers for aortic disease," vol. 6, no. 1, p. e000862, 2019.
https://doi.org/10.1136/openhrt-2018-000862
[6] E. Sanna , S. Loukogeorgakis , T. Prior , I. Derwig , G. Paramasivam , M. Choudhry, C. Lees, "Fetal abdominal cysts: antenatal course and postnatal outcomes," vol. 47, no. 4, pp. 418-421, 2019.
https://doi.org/10.1515/jpm-2018-0311
[7] T. Alelign and B. J. A. i. u. Petros, "Kidney stone disease: an update on current concepts," vol. 2018, 2018.
https://doi.org/10.1155/2018/3068365
[8] J. Hsieh, M. Purdue, S. Signoretti, C. Swanton, L. Albiges, M. Schmidinger, D. Heng, J. Larkin, V. Ficarra, "Renal cell carcinoma," vol. 3, no. 1, pp. 1-19, 2017.
https://doi.org/10.1038/nrdp.2017.9
[9] S. Safiri, A. Kolahi, M. Mansournia, A. Almasi-Hashiani, A. Ashrafi-Asgarabad, M. Sullman, D. Bettampadi, M. Qorbani, M. Moradi-Lakeh, M. Ardalan, A. Mokdad, C. Fitzmaurice, "The burden of kidney cancer and its attributable risk factors in 195 countries and territories, 1990-2017," vol. 10, no. 1, pp. 1-20, 2020.
https://doi.org/10.1038/s41598-020-70840-2
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[11] K. C. Saw, J. A. McAteer, A. G. Monga, G. T. Chua, J. E. Lingeman, and J. C. J. A. J. o. R. Williams Jr, "Helical CT of urinary calculi: effect of stone composition, stone size, and scan collimation," vol. 175, no. 2, pp. 329-332, 2000.
https://doi.org/10.2214/ajr.175.2.1750329
[12] J. Sun, L. Peng, T. Li, D. Adila, Z. Zaiman, G. Melton-Meaux, N. Ingraham, E. Murray, D. Boley, S. Switzer, J. Burns, K. Huang, T. Allen, S. Steenburg, J. Gichoya, E. Kummerfeld, C. Tignanelli, "Performance of a chest radiograph ai diagnostic tool for covid-19: A prospective observational study," vol. 4, no. 4, p. e210217, 2022.
https://doi.org/10.1148/ryai.210217
[13] M. Islam, M. Hasan, M. Hossain, M. Alam, M. Uddin, A. Soylu, "Vision transformer and explainable transfer learning models for auto detection of kidney cyst, stone and tumor from CT-radiography," vol. 12, no. 1, pp. 1-14, 2022.
https://doi.org/10.1038/s41598-022-15634-4
[14] E. Celik, M. Atalay, A. J. I. J. o. I. S. Kondiloglu, and A. i. Engineering, "The diagnosis and estimate of chronic kidney disease using the machine learning methods," vol. 4, no. Special Issue-1, pp. 27-31, 2016.
https://doi.org/10.18201/ijisae.265967
[15] S. Vijayarani, S. Dhayanand, M. J. I. J. o. C. Phil, and B. Research, "Kidney disease prediction using SVM and ANN algorithms," vol. 6, no. 2, pp. 1-12, 2015.
[16] D. C. Elton, E. B. Turkbey, P. J. Pickhardt, and R. M. J. M. P. Summers, "A deep learning system for automated kidney stone detection and volumetric segmentation on noncontrast CT scans," vol. 49, no. 4, pp. 2545-2554, 2022.
https://doi.org/10.1002/mp.15518
[17] K. M. Black, H. Law, A. Aldoukhi, J. Deng, and K. R. J. B. i. Ghani, "Deep learning computer vision algorithm for detecting kidney stone composition," vol. 125, no. 6, pp. 920-924, 2020.
https://doi.org/10.1111/bju.15035
[18] A. Nithya, A. Appathurai, N. Venkatadri, D. Ramji, and C. A. J. M. Palagan, "Kidney disease detection and segmentation using artificial neural network and multi-kernel k-means clustering for ultrasound images," vol. 149, p. 106952, 2020.
https://doi.org/10.1016/j.measurement.2019.106952
[19] J. Verma, M. Nath, P. Tripathi, K. J. P. R. Saini, and I. Analysis, "Analysis and identification of kidney stone using Kth nearest neighbour (KNN) and support vector machine (SVM) classification techniques," vol. 27, no. 3, pp. 574-580, 2017.
https://doi.org/10.1134/S1054661817030294
[20] K. Yildirim, P. Bozdag, M. Talo, O. Yildirim, M. Karabatak, U. Acharya, "Deep learning model for automated kidney stone detection using coronal CT images," vol. 135, p. 104569, 2021.
https://doi.org/10.1016/j.compbiomed.2021.104569
[21] M. Gharaibeh, D. Alzu'bi, M. Abdullah, I. Hmeidi, M. Al Nasar, L. Abualigah, A. Gandomi, "Radiology imaging scans for early diagnosis of kidney tumors: a review of data analytics-based machine learning and deep learning approaches," vol. 6, no. 1, p. 29, 2022.
https://doi.org/10.3390/bdcc6010029
[22] A. Abdelrahman and S. J. J. o. I. Viriri, "Kidney Tumor Semantic Segmentation Using Deep Learning: A Survey of State-of-the-Art," vol. 8, no. 3, p. 55, 2022.
https://doi.org/10.3390/jimaging8030055
[23] S. Sudharson, P. J. C. M. Kokil, and P. i. Biomedicine, "An ensemble of deep neural networks for kidney ultrasound image classification," vol. 197, p. 105709, 2020.
https://doi.org/10.1016/j.cmpb.2020.105709
[24] D. Alzu'bi, M. Abdullah, I. Hmeidi, R. AlAzab, M. Gharaibeh, M. El-Heis, K. Almotairi, A. Forestiero, A. Hussein, L. Abualigah, "Kidney Tumor Detection and Classification Based on Deep Learning Approaches: A New Dataset in CT Scans," vol. 2022, 2022.
https://doi.org/10.1155/2022/3861161
[25] 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, 2017, pp. 4700-4708.
https://doi.org/10.1109/CVPR.2017.243
[26] K. Fawagreh, M. M. Gaber, E. J. S. S. Elyan, and C. E. A. O. A. Journal, "Random forests: from early developments to recent advancements," vol. 2, no. 1, pp. 602-609, 2014.
https://doi.org/10.1080/21642583.2014.956265
[27] L. J. M. l. Breiman, "Random forests," vol. 45, no. 1, pp. 5-32, 2001.
https://doi.org/10.1023/A:1010933404324
[28] O. Okun and H. Priisalu, "Random forest for gene expression based cancer classification: overlooked issues," in Iberian conference on pattern recognition and image analysis, 2007, pp. 483-490: Springer.
https://doi.org/10.1007/978-3-540-72849-8_61
[29] L. J. U. o. C. Breiman, Berkeley, "Bagging predictors (technical report 421)," 1994
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A. M. Qadir and D. F. Abd, “Kidney Diseases Classification using Hybrid Transfer-Learning DenseNet201-Based and Random Forest Classifier”, KJAR, vol. 7, no. 2, pp. 131–144, Jan. 2023, doi: 10.24017/Science.2022.2.11.
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Copyright (c) 2023 Abdalbasit Mohammed Qadir, Dana Faiq Abd
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