Automatic Diagnosis of Myocarditis Disease in Cardiac MRI Modality using Deep Transformers and Explainable Artificial Intelligence

• URL: http://arxiv.org/abs/2210.14611v2
• Date: Fri, 1 Dec 2023 07:53:28 GMT
• Title: Automatic Diagnosis of Myocarditis Disease in Cardiac MRI Modality using Deep Transformers and Explainable Artificial Intelligence
• Authors: Mahboobeh Jafari, Afshin Shoeibi, Navid Ghassemi, Jonathan Heras, Sai Ho Ling, Amin Beheshti, Yu-Dong Zhang, Shui-Hua Wang, Roohallah Alizadehsani, Juan M. Gorriz, U. Rajendra Acharya, Hamid Alinejad Rokny
• Abstract summary: Myocarditis is a significant cardiovascular disease (CVD) that poses a threat to the health of many individuals. The occurrence of microbes and viruses, including the likes of HIV, plays a crucial role in the development of myocarditis disease (MCD) The proposed CADS consists of several steps, including dataset, preprocessing, feature extraction, classification, and post-processing.
• Score: 20.415917092103033
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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