The prevalence of skin melanoma is rapidly increasing as well as the recorded death cases of its patients. Automatic image segmentation tools play an important role in providing standardized computer-assisted analysis for skin melanoma patients. Current state-of-the-art segmentation methods are based on fully convolutional neural networks, which utilize an encoder-decoder approach. However, these methods produce coarse segmentation masks due to the loss of location information during the encoding layers. Inspired by Pyramid Scene Parsing Network (PSP-Net), we propose an encoder-decoder model

Recently, public healthcare systems become one of the most pivotal parts in our daily life. Resulting in an insane increase in Medical data like medical images and patient information. Having huge amount of data requires more computational power for efficient data management. In addition, data security, privacy and trustworthy have to be maintained and guaranteed. Most medical information in the last years aggregated data from a lot of devices, smart chips, tiny sensors and wearable devices. Those devices are connected through the internet, thus called Internet of Things (IoT). These devices

Purpose: Heart segmentation in cardiac magnetic resonance images is heavily used during the assessment of left ventricle global function. Automation of the segmentation is crucial to standardize the analysis. This study aims at developing a CNN-based framework to aid the clinical measurements of the left ventricle and right ventricle in cardiac magnetic resonance images. Methods: We propose a fully automated framework for localization and segmentation of the left ventricle and right ventricle in both short- and long-axis views from cardiac magnetic resonance images. The localization module

Classification of edge-on galaxies is important to astronomical studies due to our Milky Way galaxy being an edge-on galaxy. Edge-on galaxies pose a problem to classification due to their less overall brightness levels and smaller numbers of pixels. In the current work, a novel technique for the classification of edge-on galaxies has been developed. This technique is based on the mathematical treatment of galaxy brightness data from their images. A special treatment for galaxies' brightness data is developed to enhance faint galaxies and eliminate adverse effects of high brightness backgrounds

The scientific literature is full of studies that provide evidence highlighting the role of microbiome in type 2 diabetes (T2D) development and progression, still, discrepancies are evident when studying the link between certain taxonomic groupings and T2D, thus, eliminating the discrepancy between such studies is crucial to build on a robust systematic approach to identify the possible linkage between such taxonomic groups and diabetes development and progression, hence developing a potential treatment. Here we aimed to use a publicly available data set of gut and nares microbiome of

Antimicrobial resistance (AMR) is one of the ten dangers threatening our world, according to the world health organization (WHO). Nowadays, there are plenty of electronic microbial genomics and metagenomics data records that represent host-associated microbiomes. These data introduce new insights and a comprehensive understanding of the current antibiotic resistance threats and the upcoming resistance outbreak. Many bioinformatics tools have been developed to detect the AMR genes based on different annotated databases of bacterial whole genome sequences (WGS). The number and structure of

Code-Reuse Attacks (CRAs) are solid mechanisms to bypass advanced software and hardware defenses. Due to vulnerabilities found in software which allows attackers to corrupt the memory space of the vulnerable software to modify maliciously the contents of the memory; hence controlling the software to be able to run arbitrary code. The CRAs defenses either prevents the attacker from reading program code, controlling program memory space directly or indirectly through the usage of pointers. This paper provides a thorough evaluation of the current mitigation techniques against CRAs with regards to

In some machine learning applications using soft labels is more useful and informative than crisp labels. Soft labels indicate the degree of membership of the training data to the given classes. Often only a small number of labeled data is available while unlabeled data is abundant. Therefore, it is important to make use of unlabeled data. In this paper we propose an approach for Fuzzy-Input Fuzzy-Output classification in which the classifier can learn with soft-labeled data and can also produce degree of belongingness to classes as an output for each pattern. Particularly, we investigate the

The strain encoding (SENC) technique directly encodes regional strain of the heart into the acquired MR images and produces two images with two different tunings so that longitudinal strain, on the short-axis view, or circumferential strain on the long-axis view, are measured. Interleaving acquisition is used to shorten the acquisition time of the two tuned images by 50%, but it suffers from errors in the strain calculations due to inter-tunings motion of the heart. In this work, we propose a method to correct for the inter-tunings motion by estimating the motion-induced shift in the spatial

Tagged Magnetic Resonance (MR) images are considered the gold standard for evaluating the cardiac regional function. Nevertheless, the low myocardium-to-blood contrast in tagged MR images prevents accurate segmentation of the myocardium, and hence, hinders the quantitative assessment of the global function of the heart. In this work, a method for enhancing the myocardium-to-blood contrast in tagged MR images is proposed. First, the tag pattern in each input tagged MR image is removed using technique based on the image texture and the frequency components of the tag pattern to produce two