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Fine-tuned convolutional neural network for different cardiac view classification

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Abstract

In echocardiography, an electrocardiogram is conventionally utilised in the chronological arrangement of diverse cardiac views for measuring critical measurements. Cardiac view classification plays a significant role in the identification and diagnosis of cardiac disease. Early detection of cardiac disease can be cured or treated, and medical experts accomplish this. Computational techniques classify the views without any assistance from medical experts. The process of learning and training faces issues in feature selection, training and classification. Considering these drawbacks, there is an effective rank-based deep convolutional neural network (R-DCNN) for the proficient feature selection and classification of diverse views of ultrasound images (US). Significant features in the US image are retrieved using rank-based feature selection and used to classify views. R-DCNN attains 96.7% classification accuracy, and classification results are compared with the existing techniques. From the observation of the classification performance, the R-DCNN outperforms the existing state-of-the-art classification techniques.

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Data are available on request from the authors.

References

  1. Kirkpatrick JN, Vannan MA, Narula J, Lang RM (2007) Echocardiography in heart failure: applications, utility, and new horizons. J Am Coll Cardiol 50(5):381–396

    Article  Google Scholar 

  2. d’Arcy JL, Coffey S, Loudon MA, Kennedy A, Pearson-Stuttard J, Birks J et al (2016) Large-scale community echocardiographic screening reveals a major burden of undiagnosed valvular heart disease in older people: the OxVALVE Population Cohort Study. Eur Heart J 37(47):3515–3522

    Article  Google Scholar 

  3. Levy PT, Machefsky A, Sanchez AA, Patel MD, Rogal S, Fowler S et al (2016) Reference ranges of left ventricular strain measures by two-dimensional speckle-tracking echocardiography in children: a systematic review and meta-analysis. J Am Soc Echocardiogr 29(3):209–225

    Article  Google Scholar 

  4. Peng P, Lekadir K, Gooya A, Shao L, Petersen SE, Frangi AF (2016) A review of heart chamber segmentation for structural and functional analysis using cardiac magnetic resonance imaging. Magn Reson Mater Phys, Biol Med 29(2):155–195

    Article  Google Scholar 

  5. Benharash P, Buch E, Frank P, Share M, Tung R, Shivkumar K, Mandapati R (2015) Quantitative analysis of localized sources identified by focal impulse and rotor modulation mapping in atrial fibrillation. Circul Arrhythm Electrophysiol 8(3):554–561

    Article  Google Scholar 

  6. Balaji GN, Subashini TS, Chidambaram N (2015) Cardiac view classification using speed Up robust. Indian J Sci Technol 8(S7):1–5

    Article  Google Scholar 

  7. Sanchis L, Andrea R, Falces C, Poyatos S, Vidal B, Sitges M (2018) Differential clinical implications of current recommendations for the evaluation of left ventricular diastolic function by echocardiography. J Am Soc Echocardiogr 31(11):1203–1208

    Article  Google Scholar 

  8. Balaji GN, Subashini TS, Suresh A (2014) An efficient view classification of echocardiogram using morphological operations. J Theor Appl Inf Technol 67(3):732–735

    Google Scholar 

  9. Gao X, Li W, Loomes M, Wang L (2017) A fused deep learning architecture for viewpoint classification of echocardiography. Inf Fus 36:103–113

    Article  Google Scholar 

  10. Zhang J, Gajjala S, Agrawal P, Tison GH, Hallock LA, Beussink-Nelson L et al (2018) Fully automated echocardiogram interpretation in clinical practice: feasibility and diagnostic accuracy. Circulation 138(16):1623–1635

    Article  Google Scholar 

  11. Sundaresan V, Bridge CP, Ioannou C, Noble JA (2017) Automated characterization of the fetal heart in ultrasound images using fully convolutional neural networks. In: 2017 IEEE 14th international symposium on biomedical imaging (ISBI 2017). IEEE, pp 671–674

  12. Østvik A, Smistad E, Aase SA, Haugen BO, Lovstakken L (2019) Real-time standard view classification in transthoracic echocardiography using convolutional neural networks. Ultrasound Med Biol 45(2):374–384

    Article  Google Scholar 

  13. Dezaki FT, Luong C, Ginsberg T, Rohling R, Gin K, Abolmaesumi P, Tsang T (2021) Echo-syncnet: self-supervised cardiac view synchronization in echocardiography. IEEE Trans Med Imag 40(8):2092–2104. https://doi.org/10.1109/TMI.2021.3071951

    Article  Google Scholar 

  14. Madani A, Arnaout R, Mofrad M, Arnaout R (2018) Fast and accurate view classification of echocardiograms using deep learning. NPJ Digit Med 1(1):1–8

    Article  Google Scholar 

  15. Al’Aref SJ, Anchouche K, Singh G, Slomka PJ, Kolli KK, Kumar A et al (2019) Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging. Eur Heart J 40(24):1975–1986

    Article  Google Scholar 

  16. Kusunose K (2021) Steps to use artificial intelligence in echocardiography. J Echocardiogr 19(1):21–27

    Article  Google Scholar 

  17. Sengupta PP, Adjeroh DA (2018) Will artificial intelligence replace the human echocardiographer? Clinical considerations. Circulation. 2018;138:1639–1642 https://doi.org/10.1161/CIRCULATIONAHA.118.037095

  18. Kumari CU, Murthy ASD, Prasanna BL, Reddy MPP, Panigrahy AK (2021) An automated detection of heart arrhythmias using machine learning technique: SVM. Mater Today: Proc 45:1393–1398

    Google Scholar 

  19. Manogaran G, Varatharajan R, Priyan MK (2018) Hybrid recommendation system for heart disease diagnosis based on multiple kernel learning with adaptive neuro-fuzzy inference system. Multimedia Tools Appl 77(4):4379–4399

    Article  Google Scholar 

  20. Balaji GN, Subashini TS, Chidambaram N (2014) Automatic classification of cardiac views in echocardiogram using histogram and statistical features. In: International conference on information and communication technologies (ICICT 2014)

  21. Park J, Zhou S, Simopoulos C, Otsuki J, Comaniciu D (2007) Automatic cardiac view classification of echocardiogram. In: ICCV, pp 1–8

  22. Song P, Trzasko JD, Manduca A, Huang R, Kadirvel R, Kallmes DF, Chen S (2017) Improved super-resolution ultrasound microvessel imaging with spatiotemporal nonlocal means filtering and bipartite graph-based microbubble tracking. IEEE Trans Ultrason Ferroelectr Freq Control 65(2):149–167

    Article  Google Scholar 

  23. Hamer M, Batty GD (2019) Association of body mass index and waist-to-hip ratio with brain structure: UK Biobank study. Neurology 92(6):e594–e600

    Article  Google Scholar 

  24. Cilia ND, De Stefano C, Fontanella F, di Freca AS (2019) A ranking-based feature selection approach for handwritten character recognition. Pattern Recogn Lett 121:77–86

    Article  Google Scholar 

  25. Liu M, Shi J, Li Z, Li C, Zhu J, Liu S (2016) Towards better analysis of deep convolutional neural networks. IEEE Trans Vis Comput Graph 23(1):91–100

    Article  Google Scholar 

  26. Khan A, Sohail A, Zahoora U, Qureshi AS (2020) A survey of the recent architectures of deep convolutional neural networks. Artif Intell Rev 53(8):5455–5516

    Article  Google Scholar 

  27. Hanin B (2019) Universal function approximation by deep neural nets with bounded width and relu activations. Mathematics 7(10):992

    Article  Google Scholar 

  28. Shahangian B, Pourghassem H (2016) Automatic brain hemorrhage segmentation and classification algorithm based on weighted grayscale histogram feature in a hierarchical classification structure. Biocybernet Biomed Eng 36(1):217–232

    Article  Google Scholar 

  29. Galdi P, Tagliaferri R (2019) Data mining: accuracy and error measures for classification and prediction. Encyclopedia of bioinformatics and computational biology. Elsevier, pp 431–436. https://doi.org/10.1016/B978-0-12-809633-8.20474-3

    Chapter  Google Scholar 

  30. Chicco D, Jurman G (2020) The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genom 21(1):1–13

    Article  Google Scholar 

  31. Balamurugan E, Sathishkumar K (2020) Modified support vector machine based efficient virtual machine consolidation procedure for cloud data centers. J Adv Res Dyn Control Syst 12(04-Special Issue):502–508 (ISSN 1943-023X)

    Google Scholar 

  32. Ramalingam M, Sathishkumar K (2020) Design and development of cluster based stretch and shrink scheme for topology stability and load balancing in mobile ad hoc network using weighted clustering algorithm. Int J Sci Technol Res 9(01):574–578 (ISSN 2277-8616)

    Google Scholar 

  33. Sathishkumar K (2019) Efficient clustering methods and statistical approaches for gene expression data. J Adv Res Dyn Control Syst 11(11-Special Issue):440–447 (ISSN 1943-023X)

    Article  Google Scholar 

  34. Sathishkumar K (2019) Standard weight and distribution function using glowworm swarm optimization for gene expression data. In: International conference on sustainable communication network and application [ICSCN 2019] (Springer LNDECT), held at Surya Engineering College, Tamil Nadu, India during 30–31 July 2019, vol 39, pp 604–618

  35. Sathishkumar K (2019) Gene expression analysis using clustering methods: comparison analysis. In: International conference on sustainable communication network and application [ICSCN 2019] (Springer LNDECT), held at Surya Engineering College, Tamil Nadu, India during 30–31 July 2019, vol 39, pp 633–644

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Acknowledgements

We deeply acknowledge Taif University for supporting this study through Taif University Researchers Supporting Project Number (TURSP-2020/150), Taif University, Taif, Saudi Arabia.

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Authors and Affiliations

  1. Department of ECE, Y.S.R. Engineering College of Yogi Vemana University, Proddatur, Andhra Pradesh, India

    B. P. Santosh Kumar

  2. College of Computer Science and Information Science, Majmaah University, Al Majma’ah, 11952, Saudi Arabia

    Mohd Anul Haq

  3. Department of ECE, Audisankara College of Engineering and Technology, Gudur, Andhra Pradesh, India

    P. Sreenivasulu

  4. Department of ECE, SRIT, Proddatur, Andhra Pradesh, India

    D. Siva

  5. Information Technology Department, Ajloun National University, Ajloun, Jordan

    Malik Bader Alazzam

  6. Department of Computer Engineering, College of Computers and Information Technology, Taif University, Taif, Saudi Arabia

    Fawaz Alassery

  7. Gobi Arts and Science College (Autonomous), Gobichettipalayam, India

    Sathishkumar Karupusamy

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  1. B. P. Santosh Kumar
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  7. Sathishkumar Karupusamy
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Santosh Kumar, B.P., Haq, M.A., Sreenivasulu, P. et al. Fine-tuned convolutional neural network for different cardiac view classification. J Supercomput 78, 18318–18335 (2022). https://doi.org/10.1007/s11227-022-04587-0

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