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This paper presents an ECG-based human identification that aims to increase the prediction accuracy with a minimized training dataset using a Block Dense CNN (BDCNN) architecture. This study specifically examines the effect of increasing the number of individuals, while decreasing the amount of training data on the performance of the network. Furthermore, the performance of the proposed network with multisession ECG recordings is also investigated. Comprehensive experimentation on nine ECG public databases that include normal and various abnormal ECG beats has been carried out. Performance of the network is evaluated using metrics such as prediction accuracy, false rejection ratio (FRR), false acceptance ratio (FAR), computational complexity, and training time, and the results are then compared to the results of a residual network. Experimental results show that, not only in a single session but also in multisession ECG recordings, the proposed network proficiently overbears the load of increased persons with decreased training dataset with about a 5.06% increase in identification rate as compared to its close counterpart. Therefore, the proposed BDCNN can be a potential framework for ECG-based human identification systems. [ABSTRACT FROM AUTHOR]

Cardiac magnetic resonance imaging (CMRI) segmentation transforms cardiac MR images into semantic regions to define the left ventricle cavity, right ventricle cavity, and myocardium. CMRI segmentation provides ventricles' volume, mass, and ejection fraction, playing a significant role in cardiac disease diagnosis. This paper aims to propose novel deep supervision in U-Net-based architecture to enhance the segmentation performance. It presents a deeply supervised W-Net, which creates another path in parallel with the decoder path in U-Net-based architecture. The output of every upsampling layer in the decoder path is combined with pixel-wise addition for feature reuse, and loss is computed at each feature dimension on the deep supervision layer, which enables gradients to be implanted at a greater depth into the network and enhances the training of all layers in the network. Proposed W-Net applied on single scanner-based ACDC dataset and Multi-Centre, Multi-Vendor & Multi-Disease dataset, making it more robust in model generalization. W-Net significantly outperforms numerous state-of-the-art methods on the two publicly available CMRI datasets, according to experiments conducted. W-Net obtained better segmentation results ranked in the top three for many metrics. It is evident that the proposed W-Net has considerable potential in CMRI segmentation, cardiac assessment, and disease diagnosis. [ABSTRACT FROM AUTHOR]