ArXiv Preprint
Myocarditis is among the most important cardiovascular diseases (CVDs),
endangering the health of many individuals by damaging the myocardium. Microbes
and viruses, such as HIV, play a vital role in myocarditis disease (MCD)
incidence. Lack of MCD diagnosis in the early stages is associated with
irreversible complications. Cardiac magnetic resonance imaging (CMRI) is highly
popular among cardiologists to diagnose CVDs. In this paper, a deep learning
(DL) based computer-aided diagnosis system (CADS) is presented for the
diagnosis of MCD using CMRI images. The proposed CADS includes dataset,
preprocessing, feature extraction, classification, and post-processing steps.
First, the Z-Alizadeh dataset was selected for the experiments. The
preprocessing step included noise removal, image resizing, and data
augmentation (DA). In this step, CutMix, and MixUp techniques were used for the
DA. Then, the most recent pre-trained and transformers models were used for
feature extraction and classification using CMRI images. Our results show high
performance for the detection of MCD using transformer models compared with the
pre-trained architectures. Among the DL architectures, Turbulence Neural
Transformer (TNT) architecture achieved an accuracy of 99.73% with 10-fold
cross-validation strategy. Explainable-based Grad Cam method is used to
visualize the MCD suspected areas in CMRI images.
Mahboobeh Jafari, Afshin Shoeibi, Navid Ghassemi, Jonathan Heras, Abbas Khosravi, Sai Ho Ling, Roohallah Alizadehsani, Amin Beheshti, Yu-Dong Zhang, Shui-Hua Wang, Juan M. Gorriz, U. Rajendra Acharya, Hamid Alinejad Rokny
2022-10-26
