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Go to Editorial ManagerCone-beam computed tomography (CBCT) is an indispensable method that reconstructs three dimensional (3D) images. CBCT employs a mathematical technique of reconstruction, which reveals the anatomy of the patient’s body through the measurements of projections. The mathematical techniques employed in the reconstruction process are classified as; analytical, and iterative. The iterative reconstruction methods have been proven to be superior over the analytical methods, but due to their prolonged reconstruction time those methods are excluded from routine use in clinical applications. The aim of this research is to accelerate the iterative methods by performing the reconstruction process using a graphical processing unit (GPU). This method is tested on two iterative-reconstruction algorithms (IR), the algebraic reconstruction technique (ART), and the multiplicative algebraic reconstruction technique (MART). The results are compared against the traditional ART, and MART. A 3D test head phantom image is used in this research to demonstrate results of the proposed method on the reconstruction algorithms. The simulation results are executed using MATLAB (version R2018b) programming language and computer system with the following specifications: CPU core i7 (2.40 GHz) for the processing, with a NIVDIA GEFORCE GPU. Experimental results indicate, that this method reduces the reconstruction time for the iterative algorithms.
In order to avoid losing sense of sight in a large portion of the working population, Diabetic Retinopathy (DR) identification during broad examination for diabetes is crucial. To prevent blindness in the future, early illness detection and measurement of disease development are essential. DR is diagnosed through medical image analysis. After the success of Deep Learning (DL) in other applications in the real world, it is considered a vital tool for upcoming health sector applications, providing solutions with accurate results for medical image analysis. This review provides a comprehensive survey of the state-of-the-art DL models for DR detection and grading using retinal fundus photography. This review thoroughly examined and summarized 81 relevant publications that were published through IEEE Xplore, Web of Science, PubMed, and Scopus between 2018 and 2023 based on the available database with binary or multiclass CNN classification models as well as the main preprocessing techniques. According to the findings of this review, transfer learning has proven to be an excellent technique for addressing the problems of limited resources for data for DR analysis. CNN models having tens or hundreds of layers are the most frequently utilized frameworks for DR classification. The most extensively utilized datasets for DR categorization are Aptos 2019 and EyePACS. Although DL has attained or surpassed human-level DR classification accuracy, there is still more work to be done in real-world clinical procedures.
Computed tomography (CT) imaging is an important diagnostic tool. CT imaging facilitates the internal rendering of a scanned object by measuring the attenuation of beams of X-ray radiation. CT employs a mathematical technique of image reconstruction; those techniques are classified as; analytical and iterative. The iterative reconstruction (IR) methods have been proven to be superior over the analytical methods, but due to their prolonged reconstruction time, those methods are excluded from routine use in clinical applications. In this paper the reconstruction time of an IR algorithm is minimized through the employment of an adaptive region growing segmentation method that focuses the image reconstruction process on a specified region, thus ignoring unwanted pixels that increase the computation time. This method is tested on the iterative algebraic reconstruction technique (ART) algorithm. Some phantom images are used in this paper to demonstrate the effects of the segmentation process. The simulation results are executed using MATLAB (version R2018b) programming language, and a computer system with the following specifications: CPU core i7 (2.40 GHz) for processing. Simulation results indicate that this method will reduce the reconstruction time of the iterative algorithms, and will enhance the quality of the reconstructed image.
Nowadays, robotic exoskeletons demonstrated great abilities to replace traditional rehabilitation processes for activating neural abilities performed by physiotherapists. The main aim of this review study is to determine a state-of-the-art robotic exoskeleton that can be used for the rehabilitation of the lower limb of people who have mobile disabilities as a result of stroke and musculoskeletal conditions. The study presented the anatomy of the lower limb and the biomechanics of human gait to explain the mechanism of the limb, which helps in constructing a robotic exoskeleton. A state-of-the-art review of more than 100 articles related to robotic exoskeletons and their constructions, functionality, and rehabilitation capabilities are accurately implemented. Moreover, the study included a review of upper limb rehabilitation that has been studied locally and successfully applied to patients who exhibited significant improvements. Results of recent studies herald an abundant future for robotic exoskeletons used in the rehabilitation of the lower extremity. Significant improvement in the mechanism and design, as well as the quality, were observed. Also, impressive results were obtained from the performance when used by patients. This study concludes that working and improving the robotic devices continuously in accordance with the cases are necessary to be treated with the best results and the lowest cost.
Alzheimer's disease (AD) is a progressive neurodegenerative disorder that severely impacts cognitive functions such as memory, attention, and reasoning, ultimately affecting daily life. Early and accurate detection is crucial for timely intervention and management. Traditional diagnostic methods, including neuroimaging and cognitive assessments, can be expensive and time-consuming, necessitating more accessible and efficient alternatives. This study aims to develop an automated and efficient deep learning-based detection system that uses Electroencephalogram (EEG) signals to accurately classify AD and healthy individuals. A Convolutional Neural Network (CNN) model was designed to extract meaningful features from preprocessed EEG data. The architecture consists of convolutional layers with max pooling, dropout regularization, and fully connected layers to improve classification accuracy. The model was trained and evaluated on a comprehensive EEG dataset, using key performance metrics such as accuracy, recall, precision, and F1-score. The proposed CNN model achieved a high classification accuracy of 94.56%, a low loss of 0.2162, and an AUC value of 0.93828, demonstrating superior classification capability. The results indicate that the model effectively distinguishes between AD and healthy individuals, outperforming several state-of-the-art approaches. The findings highlight the potential of deep learning-based EEG analysis for AD detection, providing an accessible and cost-effective tool for early diagnosis. The high accuracy of the proposed CNN model suggests that it can assist medical professionals in making well-informed decisions, ultimately improving patient outcomes.
Lung cancer is the most common dangerous disease that, if treated late, can lead to death. It is more likely to be treated if successfully discovered at an early stage before it worsens. Distinguishing the size, shape, and location of lymphatic nodes can identify the spread of the disease around these nodes. Thus, identifying lung cancer at the early stage is remarkably helpful for doctors. Lung cancer can be diagnosed successfully by expert doctors; however, their limited experience may lead to misdiagnosis and cause medical issues in patients. In the line of computer-assisted systems, many methods and strategies can be used to predict the cancer malignancy level that plays a significant role to provide precise abnormality detection. In this paper, the use of modern learning machine-based approaches was explored. More than 70 state-of-the-art articles (from 2019 to 2024) were extensively explored to highlight the different machine learning and deep learning (DL) techniques of different models used for the detection, classification, and prediction of cancerous lung tumors. The efficient model of Tiny DL must be built to assist physicians who are working in rural medical centers for swift and rapid diagnosis of lung cancer. The combination of lightweight Convolutional Neural Networks and limited resources could produce a portable model with low computational cost that has the ability to substitute the skill and experience of doctors needed in urgent cases.
A substantial amount of research has been dedicated to improving the efficiency of heat exchangers, which are extensively utilized in electronic equipment, heating and air conditioning systems, space vehicles, thermal power systems, industrial applications, and transportation. Enhancing the efficiency of these devices can lead to significant reductions in materials, cost, and space. Constructal design offers a promising approach to optimizing various heat transfer systems, including electronic packages, by applying the constructal law to achieve optimal configurations. This review aims to examine recent advancements in the application of constructal design theory to heat exchangers and its potential for enhancing thermal performance. The most recent state-of-the-art developments are thoroughly described, along with their evaluating parameters, and recommendations for further research in this field are provided.
Face recognition and identification have recently become the most widely employed biometric authentication technologies, especially for access to persons and other security purposes. It represents one of the most significant pattern recognition technologies that uses characteristics included in facial images or videos to detect the identity of individuals. However, most of the traditional facial algorithms have faced limitations in identification and verification accuracy. As a result, this paper presents a sophisticated system for face identification adopting a novel algorithm of deep learning, namely, You Only Look Once version 8 (YOLOv8). This system can detect the face identity of different individuals with different positions with high accuracy. The YOLOv8 model has been trained for several target face images classified as training and validation images of 1190 and 255, respectively. The experimental results show a significant improvement in face identification accuracy of 99% of mean average precision, which outperforms many state-of-the-art face identification techniques.
This article provides a general up to date review of the investigation on performances and resistances of plain and fiber containing concrete structures under periodical loadings of long endurance up to fatigue failure. Structures are almost, under the frequent influences of repeated loadings such as vibrations of rotary machines, sea /river waves, wind, earthquakes and moving vehicles. Long term application of cyclic loading leads to continually slow rate degradation of the structure rigidity leading to fatigue damage. In spite of the dominant usage of concrete, worldwide, as a building material, its fatigue behavior is not straight forward. In addition, this lack of comparison is confronted for fiber fortified concrete. The article also presently a survey of the available techniques for monitoring and measurement of fatigue impressions in concrete structures founded both their impact within the treatise domain and the non-destructive inspection. Those technical means are classified into, at least, two designations, specifically, the monitoring of fatigue induced cracking and the detection of fatigue charged damage. Those techniques parameters, evaluate the changes in the mechanical and physical materials properties during the fatigue endurance, are distantly reviewed in concern of the mechanism creating the change, shortcomings, constraints, etc. The merits, dependency, feasibility, disadvantages and limitations of each technique are assessed and compared to make an index to select the appropriated e technique for fatigues fracture or failure inspection of the type fibered or not of structural concrete
In this study, previous researches were reviewed in relation to the seismic evaluation and retrofitting of an existing building. In recent years, a considerable number of researches has been undertaken to determine the performance of buildings during the seismic events. Performance based seismic design is a modern approach to earthquake resistant design of reinforcement concrete buildings. Performance based design of building structures requires rigorous non-linear static analysis. In general, nonlinear static analysis or pushover analysis was conducted as an efficient instrument for performance-based design. Pushover analysis came into practice after 1970 year. During the seismic event, a nonlinear static analysis or pushover analysis is used to analyze building under gravity loads and monotonically increasing lateral forces. These building were evaluated until a target displacement reached. Pushover analysis provides a better understanding of buildings seismic performance, also it traces the progression of damage and failure of structural components of buildings.