Key result
The proposed deep learning model utilizing GAN for data augmentation and an enhanced differential evolution algorithm achieved an accuracy of 0.908 ± 0.020 in diagnosing myocarditis from CMR images.
Why the study?
Cardiac magnetic resonance imaging for myocarditis diagnosis carries potential for operator bias, and classification models face challenges with unbalanced data and training sensitivity.
Absolute Event Rate: 0.908% vs 0.881%
A novel deep learning architecture using GAN for data augmentation and RL for training improves myocarditis detection on CMR images compared to existing methods.
Proposed DL model may reduce operator bias in CMR myocarditis diagnosis; leaves open need for prospective clinical validation.
Myocarditis is an important public health concern since it can cause heart failure and abrupt death. It can be diagnosed with magnetic resonance imaging (MRI) of the heart, a non-invasive imaging technology with the potential for operator bias. The study provides a deep learning-based model for myocarditis detection using CMR images to support medical professionals. The proposed architecture comprises a convolutional neural network (CNN), a fully-connected decision layer, a generative adversarial network (GAN)-based algorithm for data augmentation, an enhanced DE for pre-training weights, and a reinforcement learning-based method for training. We present a new method of employing produced images for data augmentation based on GAN to improve the classification performance of the provided CNN. Unbalanced data is one of the most significant classification issues, as negative samples are more than positive, decimating system performance. To solve this issue, we offer an RL-based training method that learns minority class examples with attention. In addition, we tackle the challenges associated with the training step, which typically relies on gradient-based techniques for the learning process; however, these methods often face issues like sensitivity to initialization. To start the BP process, we present an improved differential evolution (DE) technique that leverages a clustering-based mutation operator. It recognizes a successful cluster for DE and applies an original updating strategy to produce potential solutions. We assess our suggested model on the Z-Alizadeh Sani myocarditis dataset and show that it outperforms other methods.
No takes yet. Share an insight, caveat, or question.
Qing kun Zhu (2023) studied Myocarditis. Proposed deep learning model (CNN + GAN + DE + RL) vs. Other deep learning models (CNN-KCL, RLMD-PA, Danaei et al.) was evaluated on Accuracy. The proposed deep learning model utilizing GAN for data augmentation and an enhanced differential evolution algorithm achieved an accuracy of 0.908 ± 0.020 in diagnosing myocarditis from CMR images.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: