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Year 2021, Volume: 9 Issue: 2, 144 - 151, 30.04.2021
https://doi.org/10.17694/bajece.878116

Abstract

References

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  • V. Gulshan et al., "Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs," Jama, vol. 316, no. 22, pp. 2402-2410, 2016.
  • J. Song et al., "Ultrasound image analysis using deep learning algorithm for the diagnosis of thyroid nodules," Medicine, vol. 98, no. 15, 2019.
  • R. Raman et al., "Prevalence of diabetic retinopathy in India: Sankara Nethralaya diabetic retinopathy epidemiology and molecular genetics study report 2," Ophthalmology, vol. 116, no. 2, pp. 311-318, 2009.
  • S. Vijayarani, S. Dhayanand, and M. Phil, "Kidney disease prediction using SVM and ANN algorithms," International Journal of Computing and Business Research (IJCBR), vol. 6, no. 2, pp. 1-12, 2015.
  • J. Verma, M. Nath, P. Tripathi, and K. Saini, "Analysis and identification of kidney stone using K th nearest neighbour (KNN) and support vector machine (SVM) classification techniques," Pattern Recognition and Image Analysis, vol. 27, no. 3, pp. 574-580, 2017.
  • A. Nithya, A. Appathurai, N. Venkatadri, D. Ramji, and C. A. Palagan, "Kidney disease detection and segmentation using artificial neural network and multi-kernel k-means clustering for ultrasound images," Measurement, vol. 149, p. 106952, 2020.
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  • D. W. Aha, D. Kibler, and M. K. Albert, "Instance-based learning algorithms," Machine learning, vol. 6, no. 1, pp. 37-66, 1991.
  • K. P. Murphy, "Naive bayes classifiers," University of British Columbia, vol. 18, no. 60, 2006.

Kidney X-ray Images Classification using Machine Learning and Deep Learning Methods

Year 2021, Volume: 9 Issue: 2, 144 - 151, 30.04.2021
https://doi.org/10.17694/bajece.878116

Abstract

Today, kidney stone detection is done manually on medical images. This process is time-consuming and subjective as it depends on the physician. This study aims to classify healthy or patient persons according to the status of kidney stones from medical images using various machine learning methods and Convolutional Neural Networks (CNNs). We evaluated various machine learning methods such as Decision Trees (DT), Random Forest (RF), Support Vector Machines (SVC), Multilayer Perceptron (MLP), K-Nearest Neighbor (kNN), Naive Bayes (BernoulliNB), and deep neural networks using CNN. According to the experiments, the Decision Tree Classifier (DT) has the best classification result. This method has the highest F1 score rate with a success rate of 85.3% using the S+U sampling method. The experimental results show that the Decision Tree Classifier(DT) is a feasible method for distinguishing the kidney x-ray images.

References

  • D. Aune, Y. Mahamat-Saleh, T. Norat, and E. Riboli, "Body fatness, diabetes, physical activity and risk of kidney stones: a systematic review and meta-analysis of cohort studies," European journal of epidemiology, vol. 33, no. 11, pp. 1033-1047, 2018.
  • A. Ari and D. Hanbay, "Deep learning based brain tumor classification and detection system," Turkish Journal of Electrical Engineering & Computer Sciences, vol. 26, no. 5, pp. 2275-2286, 2018.
  • V. Gulshan et al., "Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs," Jama, vol. 316, no. 22, pp. 2402-2410, 2016.
  • J. Song et al., "Ultrasound image analysis using deep learning algorithm for the diagnosis of thyroid nodules," Medicine, vol. 98, no. 15, 2019.
  • R. Raman et al., "Prevalence of diabetic retinopathy in India: Sankara Nethralaya diabetic retinopathy epidemiology and molecular genetics study report 2," Ophthalmology, vol. 116, no. 2, pp. 311-318, 2009.
  • S. Vijayarani, S. Dhayanand, and M. Phil, "Kidney disease prediction using SVM and ANN algorithms," International Journal of Computing and Business Research (IJCBR), vol. 6, no. 2, pp. 1-12, 2015.
  • J. Verma, M. Nath, P. Tripathi, and K. Saini, "Analysis and identification of kidney stone using K th nearest neighbour (KNN) and support vector machine (SVM) classification techniques," Pattern Recognition and Image Analysis, vol. 27, no. 3, pp. 574-580, 2017.
  • A. Nithya, A. Appathurai, N. Venkatadri, D. Ramji, and C. A. Palagan, "Kidney disease detection and segmentation using artificial neural network and multi-kernel k-means clustering for ultrasound images," Measurement, vol. 149, p. 106952, 2020.
  • K. M. Black, H. Law, A. Aldouhki, J. Deng, and K. R. Ghani, "Deep learning computer vision algorithm for detecting kidney stone composition," BJU international, 2020.
  • J. R. Quinlan, "Induction of decision trees," Machine learning, vol. 1, no. 1, pp. 81-106, 1986.
  • L. Breiman, "Random forests," Machine learning, vol. 45, no. 1, pp. 5-32, 2001.
  • V. N. Vapnik, "An overview of statistical learning theory," IEEE transactions on neural networks, vol. 10, no. 5, pp. 988-999, 1999.
  • M. W. Gardner and S. Dorling, "Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences," Atmospheric environment, vol. 32, no. 14-15, pp. 2627-2636, 1998.
  • D. W. Aha, D. Kibler, and M. K. Albert, "Instance-based learning algorithms," Machine learning, vol. 6, no. 1, pp. 37-66, 1991.
  • K. P. Murphy, "Naive bayes classifiers," University of British Columbia, vol. 18, no. 60, 2006.
There are 15 citations in total.

Details

Primary Language English
Subjects Artificial Intelligence, Computer Software
Journal Section Araştırma Articlessi
Authors

Işıl Aksakallı 0000-0002-4156-9098

Sibel Kaçdıoğlu This is me 0000-0003-0578-998X

Y. Sinan Hanay 0000-0002-3331-5936

Publication Date April 30, 2021
Published in Issue Year 2021 Volume: 9 Issue: 2

Cite

APA Aksakallı, I., Kaçdıoğlu, S., & Hanay, Y. S. (2021). Kidney X-ray Images Classification using Machine Learning and Deep Learning Methods. Balkan Journal of Electrical and Computer Engineering, 9(2), 144-151. https://doi.org/10.17694/bajece.878116

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