The use of various kinds of magnetic resonance imaging (MRI) techniques for examining brain tissue has increased significantly in recent years, and manual investigation of each of the resulting ...images can be a time-consuming task. This paper presents an automatic brain-tumor diagnosis system that uses a CNN for detection, classification, and segmentation of glioblastomas; the latter stage seeks to segment tumors inside glioma MRI images. The structure of the developed multi-unit system consists of two stages. The first stage is responsible for tumor detection and classification by categorizing brain MRI images into normal, high-grade glioma (glioblastoma), and low-grade glioma. The uniqueness of the proposed network lies in its use of different levels of features, including local and global paths. The second stage is responsible for tumor segmentation, and skip connections and residual units are used during this step. Using 1800 images extracted from the BraTS 2017 dataset, the detection and classification stage was found to achieve a maximum accuracy of 99%. The segmentation stage was then evaluated using the Dice score, specificity, and sensitivity. The results showed that the suggested deep-learning-based system ranks highest among a variety of different strategies reported in the literature.
The successful early diagnosis of brain tumors plays a major role in improving the treatment outcomes and thus improving patient survival. Manually evaluating the numerous magnetic resonance imaging ...(MRI) images produced routinely in the clinic is a difficult process. Thus, there is a crucial need for computer-aided methods with better accuracy for early tumor diagnosis. Computer-aided brain tumor diagnosis from MRI images consists of tumor detection, segmentation, and classification processes. Over the past few years, many studies have focused on traditional or classical machine learning techniques for brain tumor diagnosis. Recently, interest has developed in using deep learning techniques for diagnosing brain tumors with better accuracy and robustness. This study presents a comprehensive review of traditional machine learning techniques and evolving deep learning techniques for brain tumor diagnosis. This review paper identifies the key achievements reflected in the performance measurement metrics of the applied algorithms in the three diagnosis processes. In addition, this study discusses the key findings and draws attention to the lessons learned as a roadmap for future research.
Visual object tracking has become one of the most active research topics in computer vision, which has been growing in commercial development as well as academic research. Many visual trackers have ...been proposed in the last two decades. Recent studies of computer vision for dynamic scenes include motion detection, object classification, environment modeling, tracking of moving objects, understanding of object behaviors, object identification, and data fusion from multiple sensors. This paper provides an in-depth overview of recent object tracking research. Object tracking tasks in realistic scenario often face challenging problems such as camera motion, occlusion, illumination effect, clutter, and similar appearance. A variety of tracker techniques have been published, which combine multiple techniques to solve multiple visual tracking sub-problems. This paper also reviews the latest research trend in object tracking based on convolutional neural networks, which is receiving growing attention. Finally, the paper discusses the future challenges and research directions for the object tracking problems that still need extensive studies in coming years.
The Internet of Things (IoT) paradigm serves as an enabler technology in several domains. Healthcare is one of the domains in which the IoT plays a vital role in increasing quality of life. On the ...one hand, the Internet of Healthcare Things (IoHT) creates smart environments and increases the efficiency and intelligence of the provided services. On the other hand, unfortunately, it suffers from security vulnerabilities inside and outside. There are various techniques used to identify, access, and securely manage IoT devices. Additionally, sensors, monitoring, key confidentiality management, integrity, and sensitive data accessibility are required. This study focuses on the IoT perception layer and offers a comprehensive review of the IoHT or the Internet of Medical Things (IoMT). The paper covers the current trends and open challenges in IoHT device authentication mechanisms, such as the physically unclonable function (PUF) and blockchain-based techniques. In addition, IoT simulators and verification tools are included. Finally, a future vision regarding the evolution of IoHT device authentication in terms of the utilization of different technologies, such as artificial intelligence, cloud computing, and 5G, is provided at end of this review.
Brain tumour is a serious disease, and the number of people who are dying due to brain tumours is increasing. Manual tumour diagnosis from magnetic resonance images (MRIs) is a time consuming process ...and is insufficient for accurately detecting, localizing, and classifying the tumour type. This research proposes a novel two-phase multi-model automatic diagnosis system for brain tumour detection and localization. In the first phase, the system structure consists of preprocessing, feature extraction using a convolutional neural network (CNN), and feature classification using the error-correcting output codes support vector machine (ECOC-SVM) approach. The purpose of the first system phase is to detect brain tumour by classifying the MRIs into normal and abnormal images. The aim of the second system phase is to localize the tumour within the abnormal MRIs using a fully designed five-layer region-based convolutional neural network (R-CNN). The performance of the first phase was assessed using three CNN models, namely, AlexNet, Visual Geometry Group (VGG)-16, and VGG-19, and a maximum detection accuracy of 99.55% was achieved with AlexNet using 349 images extracted from the standard Reference Image Database to Evaluate Response (RIDER) Neuro MRI database. The brain tumour localization phase was evaluated using 804 3D MRIs from the Brain Tumor Segmentation (BraTS) 2013 database, and a DICE score of 0.87 was achieved. The empirical work proved the outstanding performance of the proposed deep learning-based system in tumour detection compared to other non-deep-learning approaches in the literature. The obtained results also demonstrate the superiority of the proposed system concerning both tumour detection and localization.
Leading risk assessment standards such as the NIST SP 800-39 and ISO 27005 state that information security risk assessment (ISRA) is one of the crucial stages in the risk-management process. It ...pinpoints current weaknesses and potential risks, the likelihood of their materializing, and their potential impact on the functionality of critical information systems such as advanced metering infrastructure (AMI). If the current security controls are insufficient, risk assessment helps with applying countermeasures and choosing risk-mitigation strategies to decrease the risk to a controllable level. Although studies have been conducted on risk assessment for AMI and smart grids, the scientific foundations for selecting and using an appropriate method are lacking, negatively impacting the credibility of the results. The main contribution of this work is identifying an appropriate ISRA method for AMI by aligning the risk assessment criteria for AMI systems with the ISRA methodologies’ characteristics. Consequently, this work makes three main contributions. First, it presents a comprehensive comparison of multiple ISRA methods, including OCTAVE Allegro (OA), CORAS, COBRA, and FAIR, based on a variety of input requirements, tool features, and the type of risk assessment method. Second, it explores the necessary conditions for carrying out a risk assessment for an AMI system. Third, these AMI risk assessment prerequisites are aligned with the capabilities of multiple ISRA approaches to identify the best ISRA method for AMI systems. The OA method is found to be the best-suited risk assessment method for AMI, and this outcome paves the way to standardizing this method for AMI risk assessment.
This paper presents a high-performance multiple-input multiple-output (MIMO) antenna with a compact size of 42 × 42 mm
2
. The proposed antenna operates in the frequency range of 3.2–12 GHz for ultra ...wide band (UWB) wireless communication systems. The proposed MIMO antenna has four identical elements arranged to be orthogonal to each other to achieve a good performance. To achieve high isolation, stubs between the four radiating elements were used. Measured and simulated results were analyzed to obtain the antenna performance in the form of studying radiation pattern, Envelope Correlation Coefficient (ECC), diversity gain, and channel capacity loss. Additionally, simulated peak gain and total efficiency are acquired. Results indicate that the proposed antenna has UWB bandwidth, high isolation higher than 17 dB, and low correlation coefficient; therefore, this antenna is suitable for UWB wireless applications.
Automatic detection of maculopathy disease is a very important step to achieve high‐accuracy results for the early discovery of the disease to help ophthalmologists to treat patients. Manual ...detection of diabetic maculopathy needs much effort and time from ophthalmologists. Detection of exudates from retinal images is applied for the maculopathy disease diagnosis. The first proposed framework in this paper for retinal image classification begins with fuzzy preprocessing in order to improve the original image to enhance the contrast between the objects and the background. After that, image segmentation is performed through binarization of the image to extract both blood vessels and the optic disc and then remove them from the original image. A gradient process is performed on the retinal image after this removal process for discrimination between normal and abnormal cases. Histogram of the gradients is estimated, and consequently the cumulative histogram of gradients is obtained and compared with a threshold cumulative histogram at certain bins. To determine the threshold cumulative histogram, cumulative histograms of images with exudates and images without exudates are obtained and averaged for each type, and the threshold cumulative histogram is set as the average of both cumulative histograms. Certain histogram bins are selected and thresholded according to the estimated threshold cumulative histogram, and the results are used for retinal image classification. In the second framework in this paper, a Convolutional Neural Network (CNN) is utilized to classify normal and abnormal cases.
Automatic detection of maculopathy disease is a very important step to achieve high‐accuracy results for the early discovery of the disease and help ophthalmologists to treat patients. Manual detection of diabetic maculopathy needs much effort and time from ophthalmologists. Detection of exudates in eye images is applied for the maculopathy disease diagnosis. The proposed framework for retinal image classification begins with fuzzy processing for image improvement. The aim of this step is to enhance the contrast between objects and the background. After that, image segmentation is performed after binarization of the image to extract both blood vessels and the optic disc and then remove them. A gradient process is performed on the retinal images for discrimination between normal and abnormal cases. Histogram of the gradients is estimated and consequently, the cumulative histogram is obtained. Cumulative histogram of images with exudates and images without exudates are obtained. Histogram bins are thresholded to give a threshold cumulative histogram that can be used for retinal images classification. A Convolutional Neural Network (CNN) is performedutilized to classify normal and abnormal cases.
Tuberculosis is one of the most contagious and lethal illnesses in the world, according to the World Health Organization. Tuberculosis had the leading mortality rate as a result of a single ...infection, ranking above HIV/AIDS. Early detection is an essential factor in patient treatment and can improve the survival rate. Detection methods should have high mobility, high accuracy, fast detection, and low losses. This work presents a novel biomedical photonic crystal fiber sensor, which can accurately detect and distinguish between the different types of tuberculosis bacteria. The designed sensor detects these types with high relative sensitivity and negligible losses compared to other photonic crystal fiber-based biomedical sensors. The proposed sensor exhibits a relative sensitivity of 90.6%, an effective area of 4.342×10
−8
m
2
, with a negligible confinement loss of 3.13×10
−9
cm
−1
, a remarkably low effective material loss of 0.0132cm
−1
, and a numerical aperture of 0.3462. The proposed sensor is capable of operating in the terahertz regimes over a wide range (1 THz–2.4THz). An abbreviated review of non-optical detection techniques is also presented. An in-depth comparison between this work and recent related photonic crystal fiber-based literature is drawn to validate the efficacy and authenticity of the proposed design.
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