Fine-tuned convolutional neural network for different cardiac view classification

• Published in: The Journal of supercomputing Vol. 78; no. 16; pp. 18318 - 18335
• Main Authors: Santosh Kumar, B. P., Haq, Mohd Anul, Sreenivasulu, P., Siva, D., Alazzam, Malik Bader, Alassery, Fawaz, Karupusamy, Sathishkumar
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.11.2022; Springer Nature B.V
• Subjects: Artificial neural networks; Compilers; Computer Science; Echocardiography; Electrocardiography; Image classification; Interpreters; Machine learning in Intelligent Autonomous Systems; Medical imaging; Processor Architectures; Programming Languages; Training; Ranking; Neural network; Cardiac view; And ReLU; Ultrasound image; Classification
• ISSN: 0920-8542; 1573-0484
• DOI: 10.1007/s11227-022-04587-0

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.