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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References
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
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
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
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
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
Balaji GN, Subashini TS, Chidambaram N (2015) Cardiac view classification using speed Up robust. Indian J Sci Technol 8(S7):1–5
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
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
Gao X, Li W, Loomes M, Wang L (2017) A fused deep learning architecture for viewpoint classification of echocardiography. Inf Fus 36:103–113
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
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
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
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
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
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
Kusunose K (2021) Steps to use artificial intelligence in echocardiography. J Echocardiogr 19(1):21–27
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
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
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
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)
Park J, Zhou S, Simopoulos C, Otsuki J, Comaniciu D (2007) Automatic cardiac view classification of echocardiogram. In: ICCV, pp 1–8
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
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
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
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
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
Hanin B (2019) Universal function approximation by deep neural nets with bounded width and relu activations. Mathematics 7(10):992
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
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
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
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)
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)
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)
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
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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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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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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DOI: https://doi.org/10.1007/s11227-022-04587-0