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Kidney Disease Detection and Multi-Classification Usıng Deep Learning Methods

Year 2024, Volume: 5 Issue: 1, 19 - 28

Kenan Gülle Durmuş Özdemir Hasan Temurtaş

https://doi.org/10.53608/estudambilisim.1404078

Abstract

Deep learning has achieved successful results in recent years in areas such as disease and anomaly detection in the field of healthcare. When the literature is examined, the need for computer-aided software is inevitable since the diagnosis of kidney diseases is a complex, error-prone and time-consuming process. In this study, images created by a computerized tomography device were used in which patients were diagnosed with kidney stones, tumors and cysts. The images in our dataset were obtained from the open-access Kaggle platform. Classification performance was measured using the dataset, Classic CNN, ANN, ALEXNET, VGG16, VGG19 networks and the developed Poly-CNN deep learning model we proposed in the study. Extra pooling layer and connection layer were added to the CNN structure to provide more stable learning. To prevent these added layers from causing excessive learning, random neurons were disabled during training. In the deep learning models used in the study, the parameters used, layer structures, accuracy and loss graphs were examined in detail. The study showed that Poly-CNN stands out with a high accuracy rate of 99.94%. These results clearly demonstrate the effectiveness of the proposed research framework, with the Poly-CNN model outperforming other used models.

Keywords

Deep Learning Kidney Disease biomedical Poly-CNN Multiple Classification VGG

References

  • [1] Tahir, M., Naeem, A., Malik, H., Tanveer, J., Naqvi, R.A., Lee, S.W. 2023. DSCC_Net: Multi-Classification Deep Learning Models for Diagnosing of Skin Cancer Using Dermoscopic Images, Cancers (Basel)., 15(7). doi: 10.3390/cancers15072179.
  • [2] Srikantamurthy, M.M., Rallabandi, V.P.S., Dudekula, D.B., Natarajan, S., Park, J. 2023. Classification of benign and malignant subtypes of breast cancer histopathology imaging using hybrid CNN-LSTM based transfer learning, BMC Med. Imaging, 23(1), 19. doi: 10.1186/s12880-023-00964-0.
  • [3] Cifci, M. 2022. Derin Öğrenme Metodu Kullanarak BT Görüntülerinden Akciğer Kanseri Teşhisi, Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Derg., 24(71), 487–500. doi: 10.21205/deufmd.2022247114.
  • [4] Gharaibeh, M., Alzu’bi, D., Abdullah, M., Hmeidi, I., Al Nasar, M.R. Abualigah, L. Gandomi, A.H. 2022. Radiology Imaging Scans for Early Diagnosis of Kidney Tumors: A Review of Data Analytics-Based Machine Learning and Deep Learning Approaches. Big Data Cogn. Comput. 6, 29. https://doi.org/10.3390/bdcc6010029
  • [5] Aalami, N. 2020. Derin öğrenme yöntemlerini kullanarak görüntülerin analizi, Eskişehir Türk Dünyası Uygul. ve Araştırma Merk. Bilişim Derg., 1(1), 17–20.
  • [6] Liu, X., Song, L., Liu, S., Zhang, Y. 2021. A Review of Deep-Learning-Based Medical Image Segmentation Methods, Sustainability, 13(3). doi: 10.3390/su13031224.
  • [7] Özdemir, D., Tüzün, B.N. 2023. Classification of Brain Tumors With Deep Learning Models, J. Sci. Reports-A, 054, 296–306. doi: 10.59313/jsr-a.1293119.
  • [8] Özdemir, D., Arslan, N.N. 2022. Analysis of Deep Transfer Learning Methods for Early Diagnosis of the Covid-19 Disease with Chest X-ray Images, Düzce Üniversitesi Bilim ve Teknol. Derg., 10(2), 628–640. doi: 10.29130/dubited.976118.
  • [9] Krizhevsky, A., Sutskever, I., Hinton, G.E. 2012. Imagenet classification with deep convolutional neural networks, Adv. Neural Inf. Process. Syst., 25.
  • [10] Rehman, A., Naz, S., Razzak, M.I., Akram, F., Imran, M. 2020. A Deep Learning-Based Framework for Automatic Brain Tumors Classification Using Transfer Learning, Circuits Syst. Signal Process., 39(2), 757–775. doi: 10.1007/s00034-019-01246-3.
  • [11] Bingol, H., Yildirim, M., Yildirim, K., Alatas, B. 2023. Automatic classification of kidney CT images with relief based novel hybrid deep model., Peer J. Comput. Sci., 9, e1717. doi: 10.7717/peerj-cs.1717.
  • [12] N. Heller et al., 2021. The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 challenge, Med. Image Anal., 67, 101821. doi: https://doi.org/10.1016/j.media.2020.101821.
  • [13] Bhandari, M., Yogarajah, P., Kavitha, M.S., Condell, J. 2023. Exploring the Capabilities of a Lightweight CNN Model in Accurately Identifying Renal Abnormalities: Cysts, Stones, and Tumors, Using LIME and SHAP, Appl. Sci., 13(5). doi: 10.3390/app13053125.
  • [14] da Cruz, L.B., et al., 2022. Kidney tumor segmentation from computed tomography images using DeepLabv3+ 2.5D model, Expert Syst. Appl., 192, 116270. https://doi.org/10.1016/j.eswa.2021.116270.
  • [15] Alzu’bi D., et al., 2022. Kidney Tumor Detection and Classification Based on Deep Learning Approaches: A New Dataset in CT Scans, J. Healthc. Eng. 3861161. doi: 10.1155/2022/3861161.
  • [16] Islam, M.N., Hasan, M., Hossain, M.K., Alam, M. G. R., Uddin, M. Z., Soylu, A. 2022. Vision transformer and explainable transfer learning models for auto detection of kidney cyst, stone and tumor from CT-radiography, Sci. Rep., 12(1), 11440. doi: 10.1038/s41598-022-15634-4.
  • [17] Sasikaladevi, N., Revathi, A. 2024. Digital twin of renal system with CT-radiography for the early diagnosis of chronic kidney diseases, Biomed. Signal Process. Control, 88, 105632. https://doi.org/10.1016/j.bspc.2023.105632. [18] Raza A., et al., 2022. A Hybrid Deep Learning-Based Approach for Brain Tumor Classification, Electronics, 11(7). doi: 10.3390/electronics11071146.
  • [19] Yu, A.C., Mohajer, B., Eng, J. 2022. External Validation of Deep Learning Algorithms for Radiologic Diagnosis: A Systematic Review, Radiol. Artif. Intell., 4(3), e210064. doi: 10.1148/ryai.210064.
  • [20] CT Kidney Dataset: Normal-Cyst-Tumor and Stone. (2023, 06 Haziran) Erişim Adresi.” https://www.kaggle.com/datasets/nazmul0087/ct-kidney-dataset-normal-cyst-tumor-and-stone.
  • [21] Tan, M., Emeksiz, C. 2023. Yüz Tanıma Sistemleri İçin Geliştirilmiş Veri Artırma Temelli Adaptif Yüz Tanıma Modeli, Düzce Üniversitesi Bilim ve Teknol. Derg., 11(2), 588–606. doi: 10.29130/dubited.1024670.
  • [22] Koushik, J. 2016. Understanding Convolutional Neural Networks. http://arxiv.org/abs/1605.09081.
  • [23] Tosunoğlu, N.G., Benli, Y.K. 2012. Forecasting of Morgan Stanley Capital International Turkey Index with Artificial Neural Networks, Ege Acad. Rev., 12(4), 541–547.
  • [24] Krizhevsky, A., Sutskever, I., Hinton,G.E. 2017. ImageNet Classification with Deep Convolutional Neural Networks, Commun. ACM, 60(6), 84–90. doi: 10.1145/3065386.
  • [25] da Rocha, D.A., Ferreira, F. M. F., Peixoto, Z. M. A. 2022. Diabetic retinopathy classification using VGG16 neural network, Res. Biomed. Eng., 38(2), 761–772. doi: 10.1007/s42600-022-00200-8.
  • [26] Karacı, A. 2022. VGGCOV19-NET: automatic detection of COVID-19 cases from X-ray images using modified VGG19 CNN architecture and YOLO algorithm, Neural Comput. Appl., 34(10), 8253–8274. doi: 10.1007/s00521-022-06918-x.
  • [27] Balasubramaniam, S., Velmurugan, Y., Jaganathan, D., Dhanasekaran, S. 2023. A Modified LeNet CNN for Breast Cancer Diagnosis in Ultrasound Images, Diagnostics, 13(17). doi: 10.3390/diagnostics13172746.
  • [28] Maqsood, S., Damaševičius, R., Maskeliūnas, R. 2022. Multi-Modal Brain Tumor Detection Using Deep Neural Network and Multiclass SVM, Medicina (B. Aires)., 58(8). doi: 10.3390/medicina58081090.
  • [29] Rajpurkar,P., Chen, E., Banerjee, O., Topol,E.J. 2022. AI in health and medicine, Nat. Med., 28(1), 31–38. doi: 10.1038/s41591-021-01614-0.

Derin Öğrenme Yöntemleri Kullanılarak Böbrek Hastalıklarının Tespiti ve Çoklu Sınıflandırma

Year 2024, Volume: 5 Issue: 1, 19 - 28

Kenan Gülle Durmuş Özdemir Hasan Temurtaş

https://doi.org/10.53608/estudambilisim.1404078

Abstract

Derin öğrenme, sağlık alanında hastalık ve anomali tespiti gibi alanlarda son yıllarda başarılı sonuçlar elde etmiştir. Literatür incelendiğinde, böbrek hastalıklarının teşhisi, karmaşık, hata eğilimli ve zaman alıcı bir süreç olduğundan, bilgisayar destekli yazılımlara olan ihtiyaç kaçınılmazdır. Bu çalışmada, hastalara böbrek taşı, tümör ve kist teşhisi konmuş bilgisayarlı tomogrofi cihazı tarafından oluşturulan görüntüler kullanılmıştır. Veri setimizdeki görüntüler, erişime açık Kaggle platformundan elde edilmiştir. Veri seti, Classic CNN, ANN, ALEXNET, VGG16, VGG19 ağları ve çalışmada önerdiğimiz geliştirilmiş Poly-CNN derin öğrenme modeli kullanılarak sınıflandırma performansı ölçülmüştür. Daha istikrarlı öğrenme sağlamak için CNN yapısına ekstra havuzlama katmanı ve bağlantı katmanı eklenmiştir. Eklenen bu katmanlar aşırı öğrenmeye sebebiyet vermemesi için, eğitim sırasında rastgele nöronlar devre dışı bırakılmıştır. Çalışmada kullanılan derin öğrenme modellerinde, kullanılan parametreler, katman yapıları, doğruluk ve kayıp grafikleri detaylı bir şekilde incelenmiştir. Çalışmada Poly-CNN'in %99,94'lük yüksek bir doğruluk oranıyla öne çıktığı görülmüştür. Bu sonuçlar, Poly-CNN modelinin, diğer kullanılan modellerde daha iyi bir performans sergileyerek, önerilen araştırma çerçevesinin etkinliğini belirgin bir şekilde ortaya koymaktadır.

Keywords

Derin Öğrenme Böbrek Hastalığı Biyomedikal Poly-CNN Çoklu Sınıflandırma VGG

References

  • [1] Tahir, M., Naeem, A., Malik, H., Tanveer, J., Naqvi, R.A., Lee, S.W. 2023. DSCC_Net: Multi-Classification Deep Learning Models for Diagnosing of Skin Cancer Using Dermoscopic Images, Cancers (Basel)., 15(7). doi: 10.3390/cancers15072179.
  • [2] Srikantamurthy, M.M., Rallabandi, V.P.S., Dudekula, D.B., Natarajan, S., Park, J. 2023. Classification of benign and malignant subtypes of breast cancer histopathology imaging using hybrid CNN-LSTM based transfer learning, BMC Med. Imaging, 23(1), 19. doi: 10.1186/s12880-023-00964-0.
  • [3] Cifci, M. 2022. Derin Öğrenme Metodu Kullanarak BT Görüntülerinden Akciğer Kanseri Teşhisi, Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Derg., 24(71), 487–500. doi: 10.21205/deufmd.2022247114.
  • [4] Gharaibeh, M., Alzu’bi, D., Abdullah, M., Hmeidi, I., Al Nasar, M.R. Abualigah, L. Gandomi, A.H. 2022. Radiology Imaging Scans for Early Diagnosis of Kidney Tumors: A Review of Data Analytics-Based Machine Learning and Deep Learning Approaches. Big Data Cogn. Comput. 6, 29. https://doi.org/10.3390/bdcc6010029
  • [5] Aalami, N. 2020. Derin öğrenme yöntemlerini kullanarak görüntülerin analizi, Eskişehir Türk Dünyası Uygul. ve Araştırma Merk. Bilişim Derg., 1(1), 17–20.
  • [6] Liu, X., Song, L., Liu, S., Zhang, Y. 2021. A Review of Deep-Learning-Based Medical Image Segmentation Methods, Sustainability, 13(3). doi: 10.3390/su13031224.
  • [7] Özdemir, D., Tüzün, B.N. 2023. Classification of Brain Tumors With Deep Learning Models, J. Sci. Reports-A, 054, 296–306. doi: 10.59313/jsr-a.1293119.
  • [8] Özdemir, D., Arslan, N.N. 2022. Analysis of Deep Transfer Learning Methods for Early Diagnosis of the Covid-19 Disease with Chest X-ray Images, Düzce Üniversitesi Bilim ve Teknol. Derg., 10(2), 628–640. doi: 10.29130/dubited.976118.
  • [9] Krizhevsky, A., Sutskever, I., Hinton, G.E. 2012. Imagenet classification with deep convolutional neural networks, Adv. Neural Inf. Process. Syst., 25.
  • [10] Rehman, A., Naz, S., Razzak, M.I., Akram, F., Imran, M. 2020. A Deep Learning-Based Framework for Automatic Brain Tumors Classification Using Transfer Learning, Circuits Syst. Signal Process., 39(2), 757–775. doi: 10.1007/s00034-019-01246-3.
  • [11] Bingol, H., Yildirim, M., Yildirim, K., Alatas, B. 2023. Automatic classification of kidney CT images with relief based novel hybrid deep model., Peer J. Comput. Sci., 9, e1717. doi: 10.7717/peerj-cs.1717.
  • [12] N. Heller et al., 2021. The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 challenge, Med. Image Anal., 67, 101821. doi: https://doi.org/10.1016/j.media.2020.101821.
  • [13] Bhandari, M., Yogarajah, P., Kavitha, M.S., Condell, J. 2023. Exploring the Capabilities of a Lightweight CNN Model in Accurately Identifying Renal Abnormalities: Cysts, Stones, and Tumors, Using LIME and SHAP, Appl. Sci., 13(5). doi: 10.3390/app13053125.
  • [14] da Cruz, L.B., et al., 2022. Kidney tumor segmentation from computed tomography images using DeepLabv3+ 2.5D model, Expert Syst. Appl., 192, 116270. https://doi.org/10.1016/j.eswa.2021.116270.
  • [15] Alzu’bi D., et al., 2022. Kidney Tumor Detection and Classification Based on Deep Learning Approaches: A New Dataset in CT Scans, J. Healthc. Eng. 3861161. doi: 10.1155/2022/3861161.
  • [16] Islam, M.N., Hasan, M., Hossain, M.K., Alam, M. G. R., Uddin, M. Z., Soylu, A. 2022. Vision transformer and explainable transfer learning models for auto detection of kidney cyst, stone and tumor from CT-radiography, Sci. Rep., 12(1), 11440. doi: 10.1038/s41598-022-15634-4.
  • [17] Sasikaladevi, N., Revathi, A. 2024. Digital twin of renal system with CT-radiography for the early diagnosis of chronic kidney diseases, Biomed. Signal Process. Control, 88, 105632. https://doi.org/10.1016/j.bspc.2023.105632. [18] Raza A., et al., 2022. A Hybrid Deep Learning-Based Approach for Brain Tumor Classification, Electronics, 11(7). doi: 10.3390/electronics11071146.
  • [19] Yu, A.C., Mohajer, B., Eng, J. 2022. External Validation of Deep Learning Algorithms for Radiologic Diagnosis: A Systematic Review, Radiol. Artif. Intell., 4(3), e210064. doi: 10.1148/ryai.210064.
  • [20] CT Kidney Dataset: Normal-Cyst-Tumor and Stone. (2023, 06 Haziran) Erişim Adresi.” https://www.kaggle.com/datasets/nazmul0087/ct-kidney-dataset-normal-cyst-tumor-and-stone.
  • [21] Tan, M., Emeksiz, C. 2023. Yüz Tanıma Sistemleri İçin Geliştirilmiş Veri Artırma Temelli Adaptif Yüz Tanıma Modeli, Düzce Üniversitesi Bilim ve Teknol. Derg., 11(2), 588–606. doi: 10.29130/dubited.1024670.
  • [22] Koushik, J. 2016. Understanding Convolutional Neural Networks. http://arxiv.org/abs/1605.09081.
  • [23] Tosunoğlu, N.G., Benli, Y.K. 2012. Forecasting of Morgan Stanley Capital International Turkey Index with Artificial Neural Networks, Ege Acad. Rev., 12(4), 541–547.
  • [24] Krizhevsky, A., Sutskever, I., Hinton,G.E. 2017. ImageNet Classification with Deep Convolutional Neural Networks, Commun. ACM, 60(6), 84–90. doi: 10.1145/3065386.
  • [25] da Rocha, D.A., Ferreira, F. M. F., Peixoto, Z. M. A. 2022. Diabetic retinopathy classification using VGG16 neural network, Res. Biomed. Eng., 38(2), 761–772. doi: 10.1007/s42600-022-00200-8.
  • [26] Karacı, A. 2022. VGGCOV19-NET: automatic detection of COVID-19 cases from X-ray images using modified VGG19 CNN architecture and YOLO algorithm, Neural Comput. Appl., 34(10), 8253–8274. doi: 10.1007/s00521-022-06918-x.
  • [27] Balasubramaniam, S., Velmurugan, Y., Jaganathan, D., Dhanasekaran, S. 2023. A Modified LeNet CNN for Breast Cancer Diagnosis in Ultrasound Images, Diagnostics, 13(17). doi: 10.3390/diagnostics13172746.
  • [28] Maqsood, S., Damaševičius, R., Maskeliūnas, R. 2022. Multi-Modal Brain Tumor Detection Using Deep Neural Network and Multiclass SVM, Medicina (B. Aires)., 58(8). doi: 10.3390/medicina58081090.
  • [29] Rajpurkar,P., Chen, E., Banerjee, O., Topol,E.J. 2022. AI in health and medicine, Nat. Med., 28(1), 31–38. doi: 10.1038/s41591-021-01614-0.
There are 28 citations in total.

Details

Primary Language Turkish
Subjects Computer Software, Software Engineering (Other)
Journal Section Research Articles
Authors

Kenan Gülle 0000-0002-4650-1412

Durmuş Özdemir 0000-0002-9543-4076

Hasan Temurtaş 0000-0001-6738-3024

Early Pub Date February 29, 2024
Publication Date
Submission Date December 13, 2023
Acceptance Date January 30, 2024
Published in Issue Year 2024 Volume: 5 Issue: 1

Cite

IEEE K. Gülle, D. Özdemir, and H. Temurtaş, “Derin Öğrenme Yöntemleri Kullanılarak Böbrek Hastalıklarının Tespiti ve Çoklu Sınıflandırma”, Journal of ESTUDAM Information, vol. 5, no. 1, pp. 19–28, 2024, doi: 10.53608/estudambilisim.1404078.

Journal of ESTUDAM Information is indexed by Index Copernicus, Google Scholar, ASOS Index and ROAD index.