Abstract
Congenital heart diseases (CHD) can be detected through ultrasound imaging. Although ultrasound can be used for immediate diagnosis, doctors require considerable time to read dynamic clips; typically, physicians must continuously examine disease data from beating heart images. Most importantly, this type of diagnosis relies heavily on the expertise and experience of the diagnosing physician. This study established an ultrasound image classification with deep learning algorithms to overcome the challenges involved in CHD diagnosis. We detected the most common CHD, namely the first, second, and fourth types of ventricular septal defect (VSD). We improved the performance levels of well-known deep learning algorithms (InceptionV3, ResNet, and DenseNet). Because algorithm optimization and overfitting problems can influence the performance of deep learning algorithms, we studied some optimizer algorithms and early-stopping strategies. To enhance the solution quality, we used data augmentation methods for solving this classification problem. The selected approach was further compared with Google AutoML, which applies structure search for quality prediction. Our results revealed that the proposed deep learning algorithm was able to recognize most types of VSD. However, one type of VSD remains unconquered and warrants more advanced techniques.
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Acknowledgments
The data used in this study are restricted by the Research Ethics Review Committee of the Kaohsiung Veterans General Hospital with the number 19-CT8-10(190701-2) to protect participant privacy. We thank the Ministry of Science and Technology for supporting this research with ID MOST 107-2221-E-230-007 and MOST 108-2221-E-230-004.
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Chen, SH., Tai, IH., Chen, YH., Weng, KP., Hsieh, KS. (2021). Data Augmentation for a Deep Learning Framework for Ventricular Septal Defect Ultrasound Image Classification. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12664. Springer, Cham. https://doi.org/10.1007/978-3-030-68799-1_22
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