Abstract Body (Do not enter title and authors here):
Background
Myocarditis is an inflammatory disease of the myocardium resulting from a wide range of infectious or noninfectious causes. Cardiac magnetic resonance (CMR) imaging has the highest sensitivity among the non-invasive tools if performed within 2-3 weeks of the initial clinical presentation. Deep Learning (DL), an advanced subset of machine learning, can play a potential role in improving CMR abilities of diagnosing myocarditis.

Hypothesis
This project hypothesizes that an automated DL approach, using a combination of advanced network architectures, will enhance the diagnostic accuracy of CMR, thereby facilitating clinical decision-making, and minimizing human error.

Methods
DL model is pretrained on 98,898 CMR images from Kaggle dataset, which includes two categories of images, normal and sick, meaning myocarditis present or absent. The suggested approach started with a preprocessing, which included resizing CMR images to 100x100 pixels and normalizing them to a [0, 1] range, followed by DenseNet feature extraction. A flatten layer then turns multidimensional features into a one-dimensional vector, which is then processed by dense layers with ReLU activation to capture nonlinear combinations. Dropout layers improve model generalization by reducing overfitting. A SoftMax output layer for categorical predictions is used in classification; it is trained using the Adam optimizer and categorical cross-entropy loss. The model is tested on the test dataset, and the performance will be assessed using Accuracy, Precision, Area Under Receiver Operating Characteristic (AUROC) Curve.

Results
The suggested DL model with DenseNet enhancement was able to identify myocarditis from CMR images with accuracy of 97.38%, Precision of 97.35%, and AUROC curve of 0.9784 after being tested on Kaggle’s test dataset.

Conclusion
The automatic identification of myocarditis from CMR images was shown to be highly effective using the DenseNet-enhanced DL architecture. It provides a dependable and expandable non-invasive cardiac diagnosis solution through the integration of sophisticated segmentation, feature extraction, and classification approaches. The goal of this study is to improve diagnosis accuracy and minimize human error by integrating AI-driven tools into clinical workflows. Further validation with larger, more diverse datasets will refine model performance and enhance generalizability for clinical application.