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Left ventricle (LV) segmentation is essential to identify the cardiac functions for treating cardiovascular disorders. Cardiovascular magnetic resonance (CMRI) imaging is a non‐invasive technique for diagnosing cardiovascular diseases. CMRI is widely used to assess the functional integrity of the left and right ventricles for detecting changes in myocardial structure. Clinical parameters of the LV are often retrieved from CMRI scans, such as LV volumes, and ejection fraction. Moreover, manually segmenting cardiac diseases and evaluating such functions is time‐consuming and difficult for medical professionals. Deep learning networks require a lot of time, cost, and knowledge. To overcome this issue, a novel Edge and Shape feature‐based Fully Convolutional Neural Network (ES‐FCN) has been proposed for automatic LV segmentation. The ES‐FCN model segments MRI images based on edge maps instead of using gray‐scale images, which accelerates the performance of the FCN. The fuzzy‐based canny edge detection algorithm leverages fuzzy logic to detect structural changes in the LV and generate binary images. Additionally, binary‐valued kernels are used for convolution operations, where the binary values are influenced by biases derived from edge map shape descriptors. In ES‐FCN, bias values are learned from ground truth segmentation. The proposed ES‐FCN model achieves the Jaccard indices of 0.9484 ± 0.0188 for the ACDC dataset and 0.9476 ± 0.0237 for the LVSC dataset, and the dice index of 0.9319 ± 0.0188 for the ACDC dataset and 0.9314 ± 0.0237 for LVSC dataset respectively. The experimental results also reveal that the proposed ES‐FCN model is faster and requires minimal resources compared to state‐of‐the‐art models. [ABSTRACT FROM AUTHOR]

In recent decades, convolutional neural network (CNN) has become essential in many real-time applications due to its massive computational ability. But its use in portable devices is limited due to its high computation requirements. This paper proposes a novel One-Pass Processing Element (OPPE) to mitigate this limitation. The proposed OPPE removes redundant computations by eliminating those with zeros that leads to low area as well as low power consumption. The proposed OPPE model is evaluated with the help of VGG-16-based CNN accelerator. The proposed OPPE design reduces the number of four-input LUTs by 5.19%, 15.91%, 10.06% and 4.93% and the power consumption by 4.26%, 7.36%, 5.81% and 1.55% when compared with the conventional processing element (PE), activation gating PE, weight gating PE and zero gating PE, respectively. The proposed CNN accelerator design using OPPE achieves high throughput with less resource utilization. [ABSTRACT FROM AUTHOR]