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In this paper, a novel deep learning method Mamba-RAYOLO is presented, which can improve detection and classification in the processing and analysis of ECG images in real time by integrating three advanced modules. The feature extraction module in our work with a multi-branch structure during training can capture a wide range of features to ensure efficient inference and rich feature extraction. The attention mechanism module utilized in our proposed network can dynamically focus on the most relevant spatial and channel-wise features to improve detection accuracy and computational efficiency. Then, the extracted features can be refined for efficient spatial feature processing and robust feature fusion. Several sets of experiments have been carried out to test the validity of the proposed Mamba-RAYOLO and these indicate that our method has made significant improvements in the detection and classification of ECG images. The research offers a promising framework for more accurate and efficient medical ECG diagnostics. [ABSTRACT FROM AUTHOR]

This paper's primary goal is to diagnose COVID‐19 contamination based on the artificial intelligence approach automatically. We used convolutional neural network deep learning algorithm for analyzing the ECG images to detect cardiac abnormalities, consequent of the contamination by the SARS‐CoV‐2 virus, responsible for the COVID‐19 epidemic. We designed, trained, and evaluated the performance of two deep learning models (MobileNetV2 and VGG16) in detecting and distinguishing between two different classes (healthy subjects and COVID‐19 positive cases). Indeed, this virus attacks the human respiratory system, which could affect the heart system. Thus, developing a deep learning model could help for a quick and efficient diagnosis, prediction, and physician decision‐making. The performed deep learning model will be used for predicting abnormal cardiac activities consequent to the contamination by the virus. The overall classification rate achieved by the models was 99.34% and 99.67% for MobileNetV2 and VGG16, respectively. Therefore, this approach can efficiently contribute to the diagnosis of COVID‐19 contamination. [ABSTRACT FROM AUTHOR]

Myocardial Infarction (MI) is a heart disease due to lack of blood and oxygen flow to some part of the heart muscle. MI is a leading cause of death globally. To decrease the likelihood of MI complications, it is essential to diagnose and treat MI promptly. A Light Weight Multi-branch Network (LW-MN) based feature extraction and classification of MI from Electrocardiogram image is proposed. Extracting the information from Electrocardiogram images without segmenting the leads separately may result in loss of information. Segmenting the 12 leads by detecting the text above each lead will extract the information accurately without any information loss. Light Weight model used to extract features and fusing all twelve leads features using the depth fusion method. The outcome from the fusion method is fed into DenseNet-161 classification algorithm. LW-MN model acquired a classification accuracy, specificity, sensitivity, and F1-score of 96.09%, 97.33%, 97.78%, and 95.18%. [ABSTRACT FROM AUTHOR]

• A new model named multi branch fusion network was proposed to analyze 12 lead ECG images like printed ECGs and screenshots of ECGs, which is not limited by different ECG devices and their different sampling rates. • Rather than using the typical time domain electrical ECG signal, analyzing ECG images is an efficient way for MI screening, which is much similar to the physician's diagnosis process. • The proposed arch itecture enables features of ECG images to be extracted effectively and reaches human level performance with the trade off between high performance and simple prepossessing of the limited data. Background and Objective: Myocardial infarction (MI) is a myocardial anoxic incapacitation caused by severe cardiovascular obstruction that can cause irreversible injury or even death. In medical field, the electrocardiogram (ECG) is a common and effective way to diagnose myocardial infarction, which often requires a wealth of medical knowledge. It is necessary to develop an approach that can detect the MI automatically. Methods: In this paper, we propose a multi-branch fusion framework for automatic MI screening from 12-lead ECG images, which consists of multi-branch network, feature fusion and classification network. First, we use text detection and position alignment to automatically separate twelve leads from ECG images. Then, those 12 leads are input into the multi-branch network constructed by a shallow neural network to get 12 feature maps. After concatenating those feature maps by depth fusion, classification is explored to judge the given ECG is MI or not. Results: Based on extensive experiments on an ECG image dataset, performances of different combinations of structures are analyzed. The proposed network is compared with other networks and also compared with physicians in the practical use. All the experiments verify that the proposed method is effective for MI screening based on ECG images, which achieves accuracy, sensitivity, specificity and F1-score of 94.73%, 96.41%, 95.94% and 93.79% respectively. Conclusions: Rather than using the typical one-dimensional electrical ECG signal, this paper gives an effective model to screen MI by analyzing 12-lead ECG images. Extracting and analyzing these 12 leads from their corresponding ECG images is a good attempt in the application of MI screening. [ABSTRACT FROM AUTHOR]