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Machine Learning Based Techniques for Detection of Renal Calculi in Ultrasound Images

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Advances in Computing and Data Sciences (ICACDS 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1440))

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Abstract

The Ultrasound imaging is a non-invasive procedural technique which is used for detection of kidney diseases in the medical/clinical practice. This work emphasizes on different preprocessing methods to remove the speckle noise, embedded in kidney Ultrasound images. Preprocessing filters like, adaptive median and wiener are applied to both normal and renal calculi US images, evaluated for noise variance ranging from 0.01 and 0.08 against the parameters like Signal to Noise Ratio (SNR), Peak Signal to Noise Ratio (PSNR), Root Mean Square error (RMSE), Mean Squared Error (MAE), to determine the optimum noise variance value to be considered in preprocessing the Ultrasound images. The work also recommends adaptive median filter applied for kidney Ultrasound images with experimental results indicates increase in Peak Signal to Noise Ratio up to 33.51 dB, compared to Weiner filter. The next step is to select the best classifiers like Support Vector Machine with kernels, Multi-Layer Perceptron to preprocessed Ultrasound kidney images to estimate the accuracy, Recall, F1 score and precision. The experimental results obtained by Support Vector machine with poly kernel reaches an accuracy of 81.1% and are compared with results obtained from similar works.

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Authors and Affiliations

  1. RV College of Engineering, Bengaluru, Karnataka, India

    Harsha Herle & K. V. Padmaja

Authors
  1. Harsha Herle
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  2. K. V. Padmaja
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Correspondence to Harsha Herle .

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Editors and Affiliations

  1. University of KwaZulu-Natal, Durban, South Africa

    Mayank Singh

  2. Jaypee University of Engineering and Technology, Guna, Madhya Pradesh, India

    Vipin Tyagi

  3. Jaypee University of Information Technology, Waknaghat, Himachal Pradesh, India

    P. K. Gupta

  4. Institute of Information Theory and Automation, Prague, Czech Republic

    Jan Flusser

  5. University of Ottawa, Ottawa, ON, Canada

    Tuncer Ören

  6. MVPS's Karmaveer Adv. Baburao Ganpatrao Thakare College of Engineering, Nashik, Maharashtra, India

    V. R. Sonawane

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Herle, H., Padmaja, K.V. (2021). Machine Learning Based Techniques for Detection of Renal Calculi in Ultrasound Images. In: Singh, M., Tyagi, V., Gupta, P.K., Flusser, J., Ören, T., Sonawane, V.R. (eds) Advances in Computing and Data Sciences. ICACDS 2021. Communications in Computer and Information Science, vol 1440. Springer, Cham. https://doi.org/10.1007/978-3-030-81462-5_41

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  • DOI: https://doi.org/10.1007/978-3-030-81462-5_41

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-81461-8

  • Online ISBN: 978-3-030-81462-5

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