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Machine learning based biomedical image processing for echocardiographic images

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Abstract

The popularity of Artificial intelligence and machine learning have prompted researchers to use it in the recent researches. The proposed method uses K-Nearest Neighbor (KNN) algorithm for segmentation of medical images, extracting of image features for analysis by classifying the data based on the neural networks. Classification of the images in medical imaging is very important, KNN is one suitable algorithm which is simple, conceptual and computational, which provides very good accuracy in results. KNN algorithm is a unique user-friendly approach with wide range of applications in machine learning algorithms which are majorly used for the various image processing applications including classification, segmentation and regression issues of the image processing. The proposed system uses gray level co-occurrence matrix features. The trained neural network has been tested successfully on a group of echocardiographic images, errors were compared using regression plot. The results of the algorithm are tested using various quantitative as well as qualitative metrics and proven to exhibit better performance in terms of both quantitative and qualitative metrics in terms of current state -of- the-art methods in the related area. To compare the performance of trained neural network the regression analysis performed showed a good correlation.

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Acknowledgments

The author would like to thanks my family for providing the constant support for the preparation of this article. The author would like to extend the deepest and sincere thanks to BKIT, VTU and KBNU.

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Authors and Affiliations

  1. Department of Electronics and Communication, BKIT Bhalki Karnataka/VTU Belagavi, Karnataka, India

    Ayesha Heena & Nagashettappa Biradar

  2. Department of Electronics and Communication, KBN College of Engineering Kalaburagi Karnataka/VTU Belagavi, Karnataka, India

    Najmuddin M. Maroof

  3. Department of Information Systems, College of Computer Science and Information Technology, King Faisal University, Al Hasa, Saudi Arabia

    Surbhi Bhatia

  4. Department of Computer Application, Manav Rachna International Institute of Research and Studies, Faridabad, India

    Rashmi Agarwal

  5. Department of Computer Science, GL Bajaj Group of Institutions Mathura, Mathura, India

    Kanta Prasad

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  1. Ayesha Heena
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  2. Nagashettappa Biradar
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  3. Najmuddin M. Maroof
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  4. Surbhi Bhatia
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  5. Rashmi Agarwal
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  6. Kanta Prasad
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Correspondence to Ayesha Heena.

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Heena, A., Biradar, N., Maroof, N.M. et al. Machine learning based biomedical image processing for echocardiographic images. Multimed Tools Appl 82, 39601–39616 (2023). https://doi.org/10.1007/s11042-022-13516-5

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  • DOI: https://doi.org/10.1007/s11042-022-13516-5

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