Applying multiple image augmentation techniques to ECG images reduced deep learning classification accuracy from 0.818 to 0.764 and F1 score from 0.776 to 0.768 compared to no augmentation.
Does image augmentation improve the detection accuracy of deep learning models for ECG image classification?
Applying multiple image augmentation techniques can adversely impact the accuracy of deep learning models for ECG classification.
Tasa de eventos absoluta: 0.764% vs 0.818%
The advent of artificial intelligence has led to a better investigation of many complex research problems from a variety of domains. Recently deep learning approaches have emerged as cutting edge AI technologies and has been proved very effective in medical research. A large number of studies have recently used deep learning in the field of medical imaging for the detection and identification of various diseases including COVID19. In this work, we have investigated an ECG based approach for detection of COVID-19 and heart diseases using deep learning. Deep learning requires a lot of data and image augmentation is a way to enhance the size of data. In this study, we specifically examined the impact of augmenting ECG images for disease detection. Our study indicates that augmentation improves the detection accuracy to a certain extent and can adversely impact beyond that. Without augmentation, we achieved accuracy and F1 score of 0.818 and 0.776 which is reduced to 0.764 and 0.768 respectively, when multiple augmentation techniques are applied.
Anwar et al. (Mon,) conducted a other in COVID-19 and Cardiovascular Diseases (n=1,937). Image augmentation vs. No augmentation was evaluated on Classification accuracy. Applying multiple image augmentation techniques to ECG images reduced deep learning classification accuracy from 0.818 to 0.764 and F1 score from 0.776 to 0.768 compared to no augmentation.