Kidney Diseases Classification Based on CT-Radiography Image Using Convolutional Neural Network ResNet-50
Abstract
In 2022, kidney disease is impacting over 10% of the global population, which amounts over 800 million individuals. Kidney disease is projected to become the fifth most significant cause of mortality by 2040. Nephrolithiasis (kidney stones), kidney cysts, and Renal Cell Carcinoma (kidney tumor), are three kidney disease that can impair kidney function. Diagnosing kidney disease is crucial before any treatment can be given. Computer Tomography (CT) Radiography images can be used to determine the kind and severity of the disease. The obtained imaging data reveals normal findings and three specific kidney disease: cysts, tumors, and nephrolithiasis. The purpose of this research is to evaluate the efficacy of the Convolutional Neural Network (CNN) ResNet-50 with a hyperparameter tuning experiments in diagnose kidney disease based on the findings of kidney disease clasifyed. The greatest outcomes in this research were achieved with a learning rate of 0.001, dropout probability of 0.1, and mini batch size of 32. The experiments yielded perfect sensitivity, accuracy, and specificity, all reaching 100%. The computation time required was 15 hours, 53 minutes, and 34 seconds. The CNN ResNet-50 ability to accurately diagnose kidney disease is demonstrated by these result.
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C. P. Kovesdy, “Epidemiology of Chronic Kidney Disease: An Update 2022,” Kidney Int. Suppl. Vol. 12, Issue 1, April 2022, Pages 7-11, vol. 12, no. 1, pp. 7–11, 2022.
K. J. Foreman et al., “Forecasting Life Expectancy, Years of Life Lost, and All-Cause and Cause-Specific Mortality for 250 Causes of Death: Reference and Alternative Scenarios for 2016–40 for 195 Countries and Territories,” Lancet, vol. 392, no. 10159, pp. 2052–2090, 2018, doi: 10.1016/S0140-6736(18)31694-5.
S. Safiri et al., “The burden of kidney cancer and its attributable risk factors in 195 countries and territories, 1990–2017,” Sci. Rep., vol. 10, no. 1, pp. 1–20, 2020, doi: 10.1038/s41598-020-70840-2.
P. F. Wiliyanarti and A. Muhith, “Life Experience of Chronic Kidney Diseases Undergoing Hemodialysis Therapy,” J. NurseLine, vol. 4, no. 1, pp. 54–60, 2019.
I. F. Jaya, “Edukasi Pengetahuan Pembatasan Cairan pada Pasien Gagal Ginjal Kronik yang Menjalani Hemodialisis,” Indones. J. Community Serv., vol. 3, no. 2, pp. 61–68, 2023, [Online]. Available: http://ijocs.rcipublisher.org/index.php/ijocs/article/view/254/195
A. A. Salimi, “Simulasi Finite Element Analysis (FEA) dan Fabrikasi Implan untuk Perbandingan Kinerja Implan Kovensional dan Custom Cruciate Retaining (CR) untuk penderita Osteoarthritis Berdasarkan Rekonstruksi Data MRI/CT Scan Tulang Lutut,” Universitas Islam Indonesia, 2023.
Y. Widjaja et al., “Peningkatan Kewaspadaan Masyarakat Terhadap Penyakit Ginjal Kronis dengan Edukasi Gaya Hidup dan Skrining Fungsi Ginjal,” Communnity Dev. J., vol. 4, no. 6, pp. 12147–12153, 2023.
J. Sun et al., “Performance of a Chest Radiograph AI Diagnostic Tool for COVID-19: A Prospective Observational Study,” Radiol. Artif. Intell., vol. 4, no. 4, 2022, doi: 10.1148/ryai.210217.
M. M. Ahsan, S. A. Luna, and Z. Siddique, “Machine-Learning-Based Disease Diagnosis : A Comprehensive Review,” Healthcare, pp. 1–30, 2022.
S. K. Wildah, S. Agustiani, A. Mustopa, N. Wuryani, H. M. Nawawi, and R. A. Safitri, “Pengenalan Wajah Menggunakan Pembelajaran Mesin Berdasarkan Ekstraksi Fitur Pada Gambar Wajah Berkualitas Rendah,” INFOTECH J. Inform. dan Teknol., vol. 2, no. 2, pp. 95–103, 2021, doi: 10.37373/infotech.v2i2.189.
R. Tekade and K. Rajeswari, “Lung Cancer Detection and Classification using Deep Learning,” in 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), 2018, pp. 1–5.
W. Wang et al., “Detection of SARS-CoV-2 in Different Types of Clinical Specimens,” JAMA - J. Am. Med. Assoc., 2020, doi: 10.1001/jama.2020.3786.
B. Feng, H. Zhou, G. Li, Y. Zhang, K. Sood, and S. Yu, “Enabling Machine Learning with Service Function Chaining for Security Enhancement at 5G Edges,” IEEE Netw., vol. 35, pp. 196–201, 2021.
E. Suherman, D. Hindarto, A. Makmur, and H. Santoso, “Comparison of Convolutional Neural Network and Artificial Neural Network for Rice Detection,” Sinkron, vol. 8, no. 1, pp. 247–255, 2023, doi: 10.33395/sinkron.v8i1.11944.
T. Maruyama et al., “Comparison Of Medical Image Classification Accuracy Among Three Machine Learning Methods,” J. Xray. Sci. Technol., vol. 26, no. 6, pp. 885–893, 2018.
S. Naz, K. Bibi, and R. Ahmad, “DeepSignature: Fine-Tuned Transfer Learning Based Signature Verification System,” Multimed. Tools Appl., vol. 81, pp. 38113–38122, 2022, doi: 10.1007/s11042-022-12782-7.
C. Jianfei and Z. Changming, “Research on Image Recognition Based on Improved ResNet,” in 2020 IEEE 6th International Conference on Computer and Communications, 2021, pp. 1422–1426. doi: 10.1088/1742-6596/1982/1/012011.
U. Maheen, K. I. Malik, and G. Ali, “Comparative Analysis of Deep Learning Algorithms for Classification of COVID-19 X-Ray Images,” Int. J. Comput. Sci. Trends Technol., vol. 9, no. 4, pp. 73–81, 2021, [Online]. Available: www.ijcstjournal.org
V. G. Krishnan, P. A. A. Saleem, K. Sathyamoorthy, K. H. Priya, and T. K. S. Kumar, “Colorectal Cancer Prediction using ResNet-CNN Classification Method,” Int. J. Adv. Soft Comput. its Appl., vol. 15, no. 2, pp. 54–66, 2023, doi: 10.15849/IJASCA.230720.04.
S. Sumitha and S. Gokila, “Experimental Approach to Identify the Optimal Deep CNN Models to Early Detection of Glaucoma from Fundus CT-Scan Images,” 2023 7th Int. Conf. Intell. Comput. Control Syst., pp. 511–515, 2023.
S. A. Rachman, D. C. Bagaskara, R. Magdalena, and S. Sa’idah, “Classification of Pneumonia Based on X-Ray Images with ResNet-50 Architecture,” 2023.
M. Chhabra and R. Kumar, “An Efficient ResNet-50 based Intelligent Deep Learning Model to Predict Pneumonia from Medical Images,” 2022 Int. Conf. Sustain. Comput. Data Commun. Syst., pp. 1714–1721, 2022, doi: 10.1109/ICSCDS53736.2022.9760995.
S. Sukegawa et al., “Multi-Task Deep Learning Model for Classification of Dental Implant Brand and Treatment Stage using Dental Panoramic Radiograph Images,” Biomolecules, vol. 11, no. 6, 2021, doi: 10.3390/biom11060815.
L. Wen, X. Li, and L. Gao, “A Transfer Convolutional Neural Network for Fault Diagnosis Based on ResNet-50,” Neural Comput. Appl., vol. 32, pp. 6111–6124, 2020, doi: 10.1007/s00521-019-04097-w.
S. Showkat and S. Qureshi, “Efficacy of Transfer Learning-based ResNet Models in Chest X-ray Image Classification for Detecting COVID-19 Pneumonia,” Chemom. Intell. Lab. Syst., vol. 224, p. 104534, 2022, doi: 10.1016/j.chemolab.2022.104534.
D. C. R. Novitasari, P. Wulandari, and D. Z. Haq, “Cervical Cancer Diagnosis System using Convolutional Neural Network ResidualNet,” Int. J. Comput., vol. 21, no. 1, pp. 61–68, 2022, doi: 10.47839/ijc.21.1.2518.
M. N. Islam, M. Hasan, M. K. Hossain, M. G. R. Alam, M. Z. Uddin, and A. Soylu, “Vision transformer and explainable transfer learning models for auto detection of kidney cyst, stone and tumor from CT-radiography,” Sci. Rep., vol. 12, no. 11440, pp. 1–14, 2022, doi: 10.1038/s41598-022-15634-4.
A. M. Qadir and D. F. Abd, “Kidney Diseases Classification using Hybrid Transfer-Learning DenseNet201-Based and Random Forest Classifier,” Kurdistan J. Appl. Res., no. March, pp. 131–144, 2023, doi: 10.24017/science.2022.2.11.
S. N. Almuayqil, S. A. El-Ghany, A. A. A. El-Aziz, and M. Elmogy, “KidneyNet: A Novel CNN-Based Technique for the Automated Diagnosis of Chronic Kidney Diseases from CT Scans,” Electronics, vol. 13, no. 24, 2024, doi: 10.3390/electronics13244981.
A. Pimpalkar, D. K. J. B. Saini, N. Shelke, A. Balodi, G. Rapate, and M. Tolani, “Fine-tuned deep learning models for early detection and classification of kidney conditions in CT imaging,” Sci. Rep., vol. 15, no. 10741, 2025, doi: 10.1038/s41598-025-94905-2.
W. Qu, I. Balki, M. Mendez, J. Valen, J. Levman, and P. N. Tyrrell, “Assessing and mitigating the effects of class imbalance in machine learning with application to X-ray imaging,” Int. J. Comput. Assist. Radiol. Surg., vol. 15, pp. 2041–2048, 2020, doi: 10.1007/s11548-020-02260-6.
W. Albattah and R. U. Khan, “Impact of imbalanced features on large datasets,” Front. Big Data, vol. 8, no. 1455442, 2025, doi: 10.3389/fdata.2025.1455442.
S. A. A. Ismael, A. Mohammed, and H. Hefny, “An Enhanced Deep Learning Approach for Brain Cancer MRI Images Classification using Residual Networks,” Artif. Intell. Med., vol. 102, pp. 1–14, 2019, doi: 10.1016/j.artmed.2019.101779.
M. S. Khan, N. Salsabil, M. G. R. Alam, M. A. A. Dewan, and M. Z. Uddin, “CNN-XGBoost Fusion-Based Affective State Recognition using EEG Spectrogram Image Analysis,” Sci. Rep., vol. 12, no. 1, pp. 1–19, 2022, doi: 10.1038/s41598-022-18257-x.
B. W. Zan, Z. H. Han, C. Z. Xu, M. Q. Liu, and W. Z. Wang, “High-Dimensional Aerodynamic Data Modeling Using a Machine Learning Method Based on a Convolutional Neural Network,” Adv. Aerodyn., vol. 4, no. 39, pp. 1–31, 2022, doi: 10.1186/s42774-022-00128-8.
Y. Kaya and E. Gürsoy, “A Novel Multi-Head CNN Design to Identify Plant Diseases using The Fusion of RGB Images,” Ecol. Inform., vol. 75, p. 101998, 2023.
S. Das, K. Kharbanda, S. M, R. Raman, and D. E. D, “Deep Learning Architecture Based On Segmented Fundus Image Features for Classification of Diabetic Retinopathy,” Biomed. Signal Process. Control, vol. 68, p. 102600, 2021.
T. D. Tran, M. Kimura, and Y. Nakashima, “Primary Visual Cortex Inspired Feature Extraction Hardware Model,” in 2020 4th International Conference on Recent Advances in Signal Processing, Telecommunications and Computing, SigTelCom 2020, 2020, pp. 20–24. doi: 10.1109/SigTelCom49868.2020.9199057.
S. Monaco, G. Malfatti, J. C. Culham, L. Cattaneo, and L. Turella, “Decoding Motor Imagery and Action Planning in The Early Visual Cortex: Overlapping but Distinct Neural Mechanisms,” Neuroimage, vol. 218, p. 116981, 2020, doi: 10.1016/j.neuroimage.2020.116981.
K. Liu, G. Kang, N. Zhang, and B. Hou, “Breast Cancer Classification Based on Fully-Connected Layer First Convolutional Neural Networks,” IEEE Access, vol. 6, pp. 23722–23732, 2018, doi: 10.1109/ACCESS.2018.2817593.
J. V. N. Rakamawati, “Klasifikasi Diabetic Retinopathy Berdasarkan Foto Fundus Menggunakan Convolutional Neural Network (CNN) Jenis Densenet,” UIN Sunan Ampel Surabaya, 2021.
I. Wulandari, H. Yasin, and T. Widiharih, “Klasifikasi Citra Digital Bumbu Dan Rempah Dengan Algoritma Convolutional Neural Network (CNN),” J. Gaussian, vol. 9, no. 3, pp. 273–282, 2020, doi: 10.14710/j.gauss.v9i3.27416.
M. A. Bilal, Y. Ji, Y. Wang, M. P. Akhter, and M. Yaqub, “Early Earthquake Detection Using Batch Normalization Graph Convolutional Neural Network (BNGCNN),” Appl. Sci., vol. 12, no. 7548, 2022, doi: 10.3390/app12157548.
Y. Seo and K. shik Shin, “Hierarchical Convolutional Neural Networks for Fashion Image Classification,” Expert Syst. Appl., vol. 116, pp. 328–339, 2019, doi: 10.1016/j.eswa.2018.09.022.
S. Pang, S. Qiao, T. Song, J. Zhao, and P. Zheng, “An Improved Convolutional Network Architecture Based on Residual Modeling for Person Re-Identification in Edge Computing,” IEEE Access, vol. 7, pp. 106749–106760, 2019, doi: 10.1109/ACCESS.2019.2933364.
S. Ioffe and C. Szegedy, “Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift,” Int. Conf. Mach. Learn., pp. 448–456, 2015.
H. Zebua, “Pembuatan Model Deteksi Gejala Awal Penyakit Mulut dan Kuku pada Sapi Berbasis Citra Menggunakan Metode Convolutional Neural Network,” Universitas Jambi, 2023.
P. M. Allport, Applications of Fault-Tolerant Software Architecture Principles in The Detection of Adversarial Attacks. 2023.
M. Yani, B. Irawan, and C. Setiningsih, “Application of Transfer Learning Using Convolutional Neural Network Method for Early Detection of Terry’s Nail,” in Journal of Physics: Conference Series, 2019, vol. 1201, no. 1, p. 012052. doi: 10.1088/1742-6596/1201/1/012052.
T. Y. Hsiao, Y. C. Chang, H. H. Chou, and C. Te Chiu, “Filter-Based Deep-Compression with Global Average Pooling for Convolutional Networks,” J. Syst. Archit., vol. 95, pp. 9–18, 2019, doi: 10.1016/j.sysarc.2019.02.008.
D. C. R. Novitasari et al., “Detection of COVID-19 Chest X-Ray Using Support Vector Machine and Convolutional Neural Network,” Commun. Math. Biol. Neurosci., vol. 42, pp. 1–19, 2020, doi: 10.28919/cmbn/4765.
I. K. Trisiawan and Y. Yuliza, “Penerapan Multi-Label Image Classification Menggunakan Metode Convolutional Neural Network (CNN) Untuk Sortir Botol Minuman,” J. Teknol. Elektro, vol. 13, no. 1, p. 48, 2022, doi: 10.22441/jte.2022.v13i1.009.
A. Alqumboz and M. Naser, “Classification of Avocado Using Deep Learning,” Int. J. Acad. Eng. Res., vol. 3, no. 12, pp. 30–34, 2019, [Online]. Available: http://dspace.alazhar.edu.ps/xmlui/handle/123456789/563
I. Kouretas and V. Paliouras, “Simplified Hardware Implementation of the Softmax Activation Function,” in 8th International Conference on Modern Circuits and Systems Technologies, MOCAST 2019, 2019, pp. 1–4. doi: 10.1109/MOCAST.2019.8741677.
M. Kull, M. Perello-Nieto, M. Kängsepp, T. S. Filho, H. Song, and P. Flach, “Beyond Temperature Scaling: Obtaining Well-Calibrated Multiclass Probabilities with Dirichlet Calibration,” 2019.
R. Fachmi, A. Hidayatno, and Y. A. A. Soetrisno, “Sistem Identifikasi Ukuran Tubuh Menggunakan Metode Convolutional Neural Network (CNN),” Transient J. Ilm. Tek. Elektro, vol. 9, no. 1, pp. 1–7, 2020, doi: 10.14710/transient.v9i1.1-7.
A. V. Ikechukwu, S. Murali, R. Deepu, and R. C. Shivamurthy, “ResNet-50 vs VGG-19 vs training from scratch: A comparative analysis of the segmentation and classification of Pneumonia from chest X-ray images,” Glob. Transitions Proc., vol. 2, no. 2, pp. 375–381, 2021, doi: 10.1016/j.gltp.2021.08.027.
S. Raman, R. Maskeliūnas, and R. Damaševičius, “Markerless Dog Pose Recognition in The Wild using Resnet Deep Learning Model,” Computers, vol. 11, no. 2, pp. 1–12, 2021, doi: 10.3390/computers11010002.
A. Negi, K. Kumar, N. S. Chaudhari, N. Singh, and P. Chauhan, “Predictive Analytics for Recognizing Human Activities Using Residual Network and Fine-Tuning,” Big Data Anal., vol. 13147, 2021, doi: 10.1007/978-3-030-93620-4_21.
D. Sarwinda, R. H. Paradisa, A. Bustamam, and P. Anggia, “Deep Learning in Image Classification using Residual Network (ResNet) Variants for Detection of Colorectal Cancer,” Procedia Comput. Sci., vol. 179, pp. 423–431, 2021, doi: 10.1016/j.procs.2021.01.025.
L. H. Shehab, O. M. Fahmy, S. M. Gasser, and M. S. El-Mahallawy, “An Efficient Brain Tumor Image Segmentation Based on Deep Residual Networks (ResNets),” J. King Saud Univ. - Eng. Sci., vol. 33, pp. 404–412, 2021, doi: 10.1016/j.jksues.2020.06.001.
B. Yang et al., “Identification of Species by Combining Molecular and Morphological Data Using Convolutional Neural Networks,” Syst. Biol., vol. 71, no. 3, pp. 690–705, 2022, doi: 10.1093/sysbio/syab076.
M. H. A. Jihaannuriy, “Pengenalan Pelat Nomor Otomatis Menggunakan Metode Inception-ResNet v2,” UIN Syarif Hidayatullah Jakarta, 2023.
S. Wan, Y. Liang, and Y. Zhang, “Deep Convolutional Neural Networks for Diabetic Retinopathy Detection by Image Classification,” Comput. Electr. Eng., vol. 72, pp. 274–282, 2018, doi: 10.1016/j.compeleceng.2018.07.042.
L. Da Quach, N. P. Quoc, N. H. Thi, D. C. Tran, and M. F. Hassan, “Using SURF to Improve ResNet-50 Model for Poultry Disease Recognition Algorithm,” 2020 Int. Conf. Comput. Intell. ICCI 2020, no. October, pp. 317–321, 2020, doi: 10.1109/ICCI51257.2020.9247698.
M. R. Hossain, S. Afroze, N. Siddique, and M. M. Hoque, “Automatic Detection of Eye Cataract using Deep Convolution Neural Networks (DCNNs),” 2020 IEEE Reg. 10 Symp. TENSYMP 2020, no. June, pp. 1333–1338, 2020, doi: 10.1109/TENSYMP50017.2020.9231045.
J. Xu, Y. Zhang, and D. Miao, “Three-Way Confusion Matrix for Classification: A Measure Driven View,” Inf. Sci. (Ny)., vol. 507, pp. 772–794, 2020, doi: 10.1016/j.ins.2019.06.064.
K. Ali, Z. A. Shaikh, A. A. Khan, and A. A. Laghari, “Multiclass Skin Cancer Classification Using EfficientNets – A First Step Towards Preventing Skin Cancer,” Neurosci. Informatics, vol. 2, no. 4, p. 100034, 2022, doi: 10.1016/j.neuri.2021.100034.
G. Zeng, “On The Confusion Matrix in Credit Scoring and Its Analytical Properties,” Commun. Stat. - Theory Methods, vol. 47, no. 24, pp. 1–15, 2020, doi: 10.1080/03610926.2019.1568485.
H. Tabrizchi, M. M. Javidi, and V. Amirzadeh, “Estimates of Residential Building Energy Consumption using a Multi-Verse Optimizer-Based Support Vector Machine with K-Fold Cross-Validation,” Evol. Syst., vol. 12, pp. 755–767, 2021, doi: 10.1007/s12530-019-09283-8.
Y. N. Fuadah, I. D. Ubaidullah, N. Ibrahim, F. F. Taliningsing, N. K. Sy, and M. A. Pramuditho, “Optimasi Convolutional Neural Network dan K-Fold Cross Validation pada Sistem Klasifikasi Glaukoma,” ELKOMIKA J. Tek. Energi Elektr. Tek. Telekomun. Tek. Elektron., vol. 10, no. 3, pp. 728–741, 2022, doi: 10.26760/elkomika.v10i3.728.
Y. Wu et al., “Demystifying Learning Rate Policies for High Accuracy Training of Deep Neural Networks,” 2019 IEEE Int. Conf. Big Data (Big Data), pp. 1971–1980, 2019, doi: 10.1109/BigData47090.2019.9006104.
K. You, M. Long, J. Wang, and M. I. Jordan, “How Does Learning Rate Decay Help Modern Neural Networks?,” CoRR, 2019, [Online]. Available: http://arxiv.org/abs/1908.01878
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A simple way to prevent neural networks from overfitting,” J. Mach. Learn. Res., vol. 15, pp. 1929–1958, 2014.
A. Peryanto, A. Yudhana, and R. Umar, “Klasifikasi Citra Menggunakan Convolutional Neural Network dan K Fold Cross Validation,” J. Appl. Informatics Comput., vol. 4, no. 1, pp. 45–51, 2020, doi: 10.30871/jaic.v4i1.2017.
S. Cai, Y. Shu, G. Chen, B. C. Ooi, W. Wang, and M. Zhang, “Effective and Efficient Dropout for Deep Convolutional Neural Networks,” pp. 1–12, 2019, [Online]. Available: http://arxiv.org/abs/1904.03392
A. Ashiquzzaman et al., “Reduction of Overfitting in Diabetes Prediction Using Deep Learning Neural Network,” in IT Convergence and Security 2017, 2017, pp. 35–43. doi: 10.1007/978-981-10-6451-7.
S. Lee et al., “Improving Scalability of Parallel CNN Training by Adjusting Mini-Batch Size at Run-Time,” in Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019, 2019, pp. 830–839. doi: 10.1109/BigData47090.2019.9006550.
T. I. Z. M. Putra, Suprapto, and A. F. Bukhori, “Model Klasifikasi Berbasis Multiclass Classification dengan Kombinasi Indobert Embedding dan Long Short-Term Memory untuk Tweet Berbahasa Indonesia (Classification Model Based on Multiclass Classification with a Combination of Indobert Embedding and Long ,” J. Ilmu Siber dan Teknol. Digit., vol. 1, no. 1, pp. 1–28, 2022, [Online]. Available: https://doi.org/10.35912/jisted.v1i1.1509
S. S. H. Konala and S. Koppu, “A robust privacy-preserving federated framework for kidney CT image classification using transfer learning models,” Front. Artif. Intell., vol. 9, 2026, doi: 10.3389/frai.2026.1840721.
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