Key result
The Rank-based Deep Convolutional Neural Network (R-DCNN) achieved a classification accuracy of 96.7% for cardiac ultrasound views, outperforming existing machine learning techniques.
Why the study?
Computational cardiac view classification in echocardiography faces challenges in feature selection, training, and classification.
Does a rank-based deep convolutional neural network (R-DCNN) improve the classification accuracy of diverse cardiac views in ultrasound images compared to existing machine learning techniques?
Does a rank-based deep convolutional neural network (R-DCNN) improve the classification accuracy of diverse cardiac views in ultrasound images compared to existing machine learning techniques?
An effective rank-based deep convolutional neural network (R-DCNN) achieved 96.7% accuracy in classifying diverse echocardiographic views, outperforming existing state-of-the-art machine learning techniques.
May support automated echocardiographic view classification; leaves open prospective clinical validation before adoption.
In echocardiography, an electrocardiogram is conventionally utilized in the chronological arrangement of diverse cardiac views for measuring critical measurements. Cardiac view classification plays a significant role in the identification anddiagnosis 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, 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 selectionand 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-art classification techniques.
No takes yet. Share an insight, caveat, or question.
P et al. (2021) studied Cardiac view classification (n=600). Rank-based Deep Convolutional Neural Network (R-DCNN) vs. SVM, BPNN, Neuro-fuzzy, and ML-boosting was evaluated on Classification accuracy. The Rank-based Deep Convolutional Neural Network (R-DCNN) achieved a classification accuracy of 96.7% for cardiac ultrasound views, outperforming existing machine learning techniques.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: