Chest X-ray (CXR) is a low-cost medical imaging technique. It is a common procedure for the identification of many respiratory diseases compared to MRI, CT, and PET scans. This paper presents the use of generative adversarial networks (GAN) to perform the task of lung segmentation on a given CXR. GANs are popular to generate realistic data by learning the mapping from one domain to another. In our work, the generator of the GAN is trained to generate a segmented mask of a given input CXR. The discriminator distinguishes between a ground truth and the generated mask, and updates the generator through the adversarial loss measure. The objective is to generate masks for the input CXR, which are as realistic as possible compared to the ground truth masks. The model is trained and evaluated using four different discriminators referred to as D1, D2, D3 and D4 respectively. Experimental results on three different CXR datasets reveal that the proposed model is able to achieve a dice-score of 0.9740, and IOU score of 0.943, which are better than other reported state-of-the art results. INDEX TERMS Deep learning, generative adversarial networks, lung segmentation, medical imaging.

In this paper, a high speed elliptic curve cryptographic (ECC) processor for National Institute of Standards and Technology (NIST) recommended prime [Formula: see text] is proposed. The modular arithmetic components in the proposed ECC processor are highly optimized at both architectural level and circuit level. Redundant-signed-digit (RSD) arithmetic is adopted in the modular arithmetic components to avoid lengthy carry propagation delay. A high speed modular multiplier is designed based on an efficient segmentation and pipelining strategy. The clock cycle count is reduced as result of the segmentation, whereas operating frequency and throughput are significantly increased due to the pipelining. An optimized pipelined architecture for modular division is also presented which is suitable for the design of ECC processor using projective coordinates. The Joye’s double and add (DAA) algorithm based on [Formula: see text]-only common [Formula: see text] (co-[Formula: see text]) coordinate is adopted at the system level for its regular and efficient behavior. The proposed ECC processor is flexible and can be implemented using any field programmable gate array (FPGA) family or standard cell libraries. The proposed ECC processor executes a single elliptic curve (EC) point multiplication (PM) operation in 0.47[Formula: see text]ms at a maximum frequency of 327[Formula: see text]MHz on Virtex-6 FPGA. The implementation results demonstrate that the proposed ECC processor outperforms the other contemporary designs reported in the literature in terms of speed and [Formula: see text] metrics.

SummaryThis workpresents a novel high‐speed redundant‐signed‐digit (RSD)‐based elliptic curve cryptographic (ECC) processor for arbitrary curves over a general prime field. The proposed ECC processor works for any value of the prime number and curve parameters. It is based on a new high speed Montgomery multiplier architecture which uses different parallel computation techniques at both circuit level and architectural level. At the circuit level, RSD and carry save techniques are adopted while pre‐computation logic is incorporated at the architectural level. As a result of these optimization strategies, the proposed Montgomery multiplier offers a significant reduction in computation time over the state‐of‐the‐art. At the system level, to further enhance the overall performance of the proposed ECC processor, Montgomery ladder algorithm with (X,Y)‐only common Z coordinate (co‐Z) arithmetic is adopted. The proposed ECC processor is synthesized and implemented on different Xilinx Virtex (V) FPGA families for field sizes of 256 to 521 bits. On V‐6 platform, it computes a single 256 to 521 bits scalar point multiplication operation in 0.65 to 2.6 ms which is up to 9 times speed‐up over the state‐of‐the‐art.

For medical image analysis, there is always an immense need for rich details in an image. Typically, the diagnosis will be served best if the fine details in the image are retained and the image is available in high resolution. In medical imaging, acquiring high-resolution images is challenging and costly as it requires sophisticated and expensive instruments, trained human resources, and often causes operation delays. Deep learning based super resolution techniques can help us to extract rich details from a low-resolution image acquired using the existing devices. In this paper, we propose a new Generative Adversarial Network (GAN) based architecture for medical images, which maps low-resolution medical images to high-resolution images. The proposed architecture is divided into three steps. In the first step, we use a multi-path architecture to extract shallow features on multiple scales instead of single scale. In the second step, we use a ResNet34 architecture to extract deep features and upscale the features map by a factor of two. In the third step, we extract features of the upscaled version of the image using a residual connection-based mini-CNN and again upscale the feature map by a factor of two. The progressive upscaling overcomes the limitation for previous methods in generating true colors. Finally, we use a reconstruction convolutional layer to map back the upscaled features to a high-resolution image. Our addition of an extra loss term helps in overcoming large errors, thus, generating more realistic and smooth images. We evaluate the proposed architecture on four different medical image modalities: (1) the DRIVE and STARE datasets of retinal fundoscopy images, (2) the BraTS dataset of brain MRI, (3) the ISIC skin cancer dataset of dermoscopy images, and (4) the CAMUS dataset of cardiac ultrasound images. The proposed architecture achieves superior accuracy compared to other state-of-the-art super-resolution architectures.