Myocarditis is a significant cardiovascular disease (CVD) that poses a threat to the health of many individuals by causing damage to the myocardium. The occurrence of microbes and viruses, including the likes of HIV, plays a crucial role in the development of myocarditis disease (MCD). The images produced during cardiac magnetic resonance imaging (CMRI) scans are low contrast, which can make it challenging to diagnose cardiovascular diseases. In other hand, checking numerous CMRI slices for each CVD patient can be a challenging task for medical doctors. To overcome the existing challenges, researchers have suggested the use of artificial intelligence (AI)-based computer-aided diagnosis systems (CADS). The presented paper outlines a CADS for the detection of MCD from CMR images, utilizing deep learning (DL) methods. The proposed CADS consists of several steps, including dataset, preprocessing, feature extraction, classification, and post-processing. First, the Z-Alizadeh dataset was selected for the experiments. Subsequently, the CMR images underwent various preprocessing steps, including denoising, resizing, as well as data augmentation (DA) via CutMix and MixUp techniques. In the following, the most current deep pre-trained and transformer models are used for feature extraction and classification on the CMR images. The findings of our study reveal that transformer models exhibit superior performance in detecting MCD as opposed to pre-trained architectures. In terms of DL architectures, the Turbulence Neural Transformer (TNT) model exhibited impressive accuracy, reaching 99.73% utilizing a 10-fold cross-validation approach. Additionally, to pinpoint areas of suspicion for MCD in CMRI images, the Explainable-based Grad Cam method was employed.

翻译：心肌炎是一种严重的心血管疾病，通过损害心肌对许多个体的健康构成威胁。微生物和病毒（包括HIV等）的发生在心肌炎疾病的发展中起着关键作用。心脏磁共振成像扫描产生的图像对比度较低，这可能给心血管疾病的诊断带来挑战。另一方面，为每位心血管疾病患者检查大量CMRI切片对医生而言也是一项艰巨任务。为克服现有挑战，研究人员提出采用基于人工智能的计算机辅助诊断系统。本文提出了一种利用深度学习方法从CMR图像中检测MCD的CADS。所提出的CADS包含数据集、预处理、特征提取、分类和后处理等多个步骤。首先，选取Z-Alizadeh数据集进行实验。随后，CMR图像经过多种预处理步骤，包括去噪、尺寸调整以及通过CutMix和MixUp技术进行数据增强。接着，采用当前最先进的深度预训练模型和Transformer模型对CMR图像进行特征提取和分类。研究结果表明，Transformer模型在检测MCD方面表现出优于预训练架构的性能。在深度学习架构中，Turbulence Neural Transformer模型通过10折交叉验证取得了99.73%的惊人准确率。此外，为定位CMRI图像中MCD的可疑区域，本研究采用了基于可解释性的Grad Cam方法。