Automatic Diagnosis of Myocarditis in Cardiac Magnetic Images Using CycleGAN and Deep PreTrained Models

Myocarditis is a cardiovascular disease caused by infectious agents, especially viruses. Compared to other cardiovascular diseases, myocarditis is very rare, occurring mainly due to chest pain or heart failure. Cardiac magnetic resonance (CMR) imaging is a popular technique for diagnosis of myocarditis. Factors such as low contrast, different noises, and high CMR slices of each patient cause many challenges when diagnosing myocarditis by specialist physicians. Therefore, it is necessary to introduce new artificial intelligence (AI) techniques for diagnosis of myocarditis from CMR images. This paper presents a new method to detect myocarditis in CMR images using deep learning (DL) models. First, the Z-Alizadeh Sani myocarditis dataset was used for simulations, which included CMR images of normal subjects and myocardial infarction patients. Next, preprocessing is performed on CMR images. CMR images are created with the help of the cycle generative adversarial network (GAN) model at this step. Finally, pretrained models including EfficientNet B3, EfficientNet V2, HrNet, ResNetrs50, ResNest50d, and ResNet 50d have been used to classify the input data. Among pretrained methods, the EfficientNet V2 model has achieved 99.33% accuracy.