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An Efficient CNN-Based Hybrid Classification and Segmentation Approach for COVID-19 Detection

Computers, materials & Continua 2022年第3期70卷 4393-4410页

作者： Abeer D.Algarni Walid el-Shafai ghada m.el banby Fathi E.Abd el-Samie Naglaa F.Soliman Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh 84428Saudi Arabia Department of electronics and electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf 32952Egypt Department of Industrial electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf 32952Egypt Department of electronics and Communications Engineering Faculty of EngineeringZagazig UniversityZagazigSharqia 44519Egypt

COVID-19 remains to proliferate precipitously in the *** has significantly influenced public health,the world economy,and the persons’***,there is a need to speed up the diagnosis and precautions to deal with COVID-19 *** this explosion of this pandemic,there is a need for automated diagnosis tools to help specialists based onmedical *** paper presents a hybrid Convolutional Neural Network(CNN)-based classification and segmentation approach for COVID-19 detection from Computed Tomography(CT)*** proposed approach is employed to classify and segment the COVID-19,pneumonia,and normal CT *** classification stage is firstly applied to detect and classify the input medical CT ***,the segmentation stage is performed to distinguish between pneumonia and COVID-19 CT *** classification stage is implemented based on a simple and efficient CNN deep learning *** model comprises four Rectified Linear Units(RelUs),four batch normalization layers,and four convolutional(Conv)*** layer depends on filters with sizes of 64,32,16,and 8.A2×2windowand a stride of 2 are employed in the utilized four max-pooling layers.A soft-max activation function and a Fully-Connected(FC)layer are utilized in the classification stage to perform the detection *** the segmentation process,the Simplified Pulse Coupled Neural Network(SPCNN)is utilized in the proposed hybrid *** proposed segmentation approach is based on salient object detection to localize the COVID-19 or pneumonia region,*** summarize the contributions of the paper,we can say that the classification process with a CNN model can be the first stage a highly-effective automated diagnosis *** the images are accepted by the system,it is possible to perform further processing through a segmentation process to isolate the regions of interest in the *** region of interest can be assesses both automatically and through ***

关键词： COVID-19 segmentation classification CNN SPCNN CT images

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An Efficient GCD-Based Cancelable Biometric Algorithm for Single and multiple Biometrics

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Computers, materials & Continua 2021年第11期69卷 1571-1595页

作者： Naglaa F.Soliman Abeer D.Algarni Walid el-Shafai Fathi E.Abd el-Samie ghada m.el banby Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department of electronics and Communications Faculty of EngineeringZagazig UniversityZagazig44519Egypt Department of electronics and electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Industrial electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt

Cancelable biometrics are required in most remote access applications that need an authentication stage such as the cloud and Internet of Things(IoT)*** objective of using cancelable biometrics is to save the original ones from hacking attempts.A generalized algorithm to generate cancelable templates that is applicable on both single and multiple biometrics is proposed in this paper to be considered for cloud and IoT *** original biometric is blurred with two co-prime ***,it can be recovered as the Greatest Common Divisor(GCD)between its two blurred *** changes if induced in the biometric image prior to processing with co-prime operators prevents the recovery of the original biometric image through a GCD ***,the ability to change cancelable templates is guaranteed,since the owner of the biometric can pre-determine and manage the minimal change induced in the biometric ***,we test the utility of the proposed algorithm in the single-and multi-biometric *** multi-biometric scenario depends on compressing face,fingerprint,iris,and palm print images,simultaneously,to generate the cancelable *** metrics such as Equal Error Rate(EER)and Area and Receiver Operator Characteristic curve(AROC)are *** results on single-and multi-biometric scenarios show high AROC values up to 99.59%,and low EER values down to 0.04%.

关键词： Cloud IoT cancelable biometrics GCD single-and multi-biometrics security applications

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Enhanced Robotic Vision System Based on Deep Learning and Image Fusion

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Computers, materials & Continua 2022年第10期73卷 1845-1861页

作者： E.A.Alabdulkreem Ahmed Sedik Abeer D.Algarni ghada m.el banby Fathi E.Abd el-Samie Naglaa F.Soliman Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department of the Robotics and Intelligent machines Faculty of Artificial IntelligenceKafrelSheikh UniversityKafrelsheikh33511Egypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department of Industrial electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department electronics and electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of electronics and Communications Faculty of EngineeringZagazig UniversityZagazig44519Egypt

Image fusion has become one of the interesting fields that attract researchers to integrate information from different image *** is involved in several *** of the recent applications is the robotic *** application necessitates image enhancement of both infrared(IR)and visible *** paper presents a Robot Human Interaction System(RHIS)based on image fusion and deep *** basic objective of this system is to fuse visual and IR images for efficient feature extraction from the captured ***,an enhancement model is carried out on the fused image to increase its *** image enhancement models such as fuzzy logic,Convolutional Neural Network(CNN)and residual network(ResNet)pre-trained model are utilized on the fusion results and they are compared with each other and with the state-of-the-art *** results prove that the fuzzy logic enhancement gives the best results from the image quality ***,the proposed system can be considered as an efficient solution for the robotic vision problem with multi-modality images.

关键词： Deep learning fuzzy logic image fusion IR images

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Efficient Deep Learning modalities for Object Detection from Infrared Images

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Computers, materials & Continua 2022年第8期72卷 2545-2563页

作者： Naglaa F.Soliman E.A.Alabdulkreem Abeer D.Algarni ghada m.el banby Fathi E.Abd el-Samie Ahmed Sedik Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department of electronics and Communications Faculty of EngineeringZagazig UniversityZagazig44519Egypt Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department of Industrial electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of electronics and electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of the Robotics and Intelligent machines Faculty of Artificial IntelligenceKafrelSheikh UniversityKafr el-sheikhEgypt

For military warfare purposes,it is necessary to identify the type of a certain weapon through video stream tracking based on infrared(IR)video *** vision is a visual search trend that is used to identify objects in images or video *** military applications,drones take a main role in surveillance tasks,but they cannot be confident for longtime ***,there is a need for such a system,which provides a continuous surveillance task to support the drone *** a system can be called a Hybrid Surveillance System(HSS).This system is based on a distributed network of wireless sensors for continuous *** addition,it includes one or more drones to make short-time missions,if the sensors detect a suspicious *** paper presents a digital solution to identify certain types of concealed weapons in surveillance applications based on Convolutional Neural Networks(CNNs)and Convolutional Long Short-Term memory(ConvLSTm).Based on initial results,the importance of video frame enhancement is obvious to improve the visibility of objects in video *** accuracy of the proposed methods reach 99%,which reflects the effectiveness of the presented *** addition,the experimental results prove that the proposed methods provide superior performance compared to traditional ones.

关键词： Deep learning object detection military applications OFDm SPIHT IoT

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An Efficient CNN-Based Automated Diagnosis Framework from COVID-19 CT Images

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Computers, materials & Continua 2021年第10期69卷 1323-1341页

作者： Walid el-Shafai Noha A.el-Hag ghada m.el-banby Ashraf A.m.Khalaf Naglaa F.Soliman Abeer D.Algarni Fathi E.Abd el-Samie Department electronics and electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of electronics and electrical Communications Faculty of EngineeringMinia UniversityEgypt Department of Industrial electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Corona Virus Disease-2019(COVID-19)continues to spread rapidly in the *** has dramatically affected daily lives,public health,and the world *** paper presents a segmentation and classification framework of COVID-19 images based on deep ***,the classification process is employed to discriminate between COVID-19,non-COVID,and pneumonia by Convolutional Neural Network(CNN).Then,the segmentation process is applied for COVID-19 and pneumonia CT ***,the resulting segmented images are used to identify the infected region,whether COVID-19 or *** proposed CNN consists of four Convolutional(Conv)layers,four batch normalization layers,and four Rectified Linear Units(RelUs).The sizes of Conv layer used filters are 8,16,32,and *** maxpooling layers are employed with a stride of 2 and a 2×2 *** classification layer comprises a Fully-Connected(FC)layer and a soft-max activation function used to take the classification decision.A novel saliencybased region detection algorithm and an active contour segmentation strategy are applied to segment COVID-19 and pneumonia CT *** acquired findings substantiate the efficacy of the proposed framework for helping the specialists in automated diagnosis applications.

关键词： Classification segmentation COVID-19 CNN deep learning diagnosis applications

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Gauss Gradient and SURF Features for Landmine Detection from GPR Images

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Computers, materials & Continua 2022年第6期71卷 4457-4486页

作者： Fatma m.el-Ghamry Walid el-Shafai mahmouad I.Abdalla ghada m.el-banby Abeer D.Algarni moawad I.Dessouky Adel S.elfishawy Fathi E.Abd el-Samie Naglaa F.Soliman Department of electronics and Communications Engineering Faculty of EngineeringZagazig UniversityZagazigSharqia44519Egypt Department of electronics and electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenoufia32952Egypt Department of Industrial electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia

Recently,ground-penetrating radar(GPR)has been extended as a well-known area to investigate the subsurface ***,its output has a low resolution,and it needs more processing for more *** paper presents two algorithms for landmine detection from GPR *** first algorithm depends on a multi-scale technique.A Gaussian kernel with a particular scale is convolved with the image,and after that,two gradients are estimated;horizontal and vertical ***,histogram and cumulative histogram are estimated for the overall gradient *** bin values on the cumulative histogram are used for discrimination between images with and without ***,a neural classifier is used to classify images with cumulative histograms as feature *** second algorithm is based on scale-space analysis with the number of speeded-up robust feature(SURF)points as the key parameter for *** addition,this paper presents a framework for size reduction of GPR images based on decimation for efficient *** further classification steps can be performed on images after *** sensitivity of classification accuracy to the interpolation process is studied in detail.

关键词： GPR images cumulative histogram gradient image neural classifier SURF

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Safety of intravitreal triamcinolone acetonide:an electrophysiologic and histopathological study in rabbits

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International Journal of Ophthalmology(English edition) 2013年第6期6卷 790-795页

作者： Laila Hassan m.el-Shazly Amal Ahmad el-Gohary ghada Ghanem.el-Hossary Department of Ophthalmology Memorial Institute of Ophthalmology Department of Physiology Research Institute ofOphthalmology Department of Pharmacology Research Institute ofOphthalmology

AIm:To evaluate the retinal safety of various doses of intravitreal triamcinolone acetonide(TA)in ***:Thirty New Zealand albino rabbits were divided into five groups(six animals each).In group 1(control group),each animal received a single intravitreal injection of 0.1 mL phosphate buffered *** groups2,3,4 and 5,each rabbit received a single intravitreal injection of 4,8,16 and 32mg of TA,*** dose was contained in 0.1 mL phosphate buffered *** ocular examinations were performed before the injection and on the 1st,3rd,10th and 17th postinjection days.A standard dark adapted electroretinogram(ERG)was obtained before injection and on the 3rd,10th and 17th post-injection ***17d,animals were sacrificed and their eyes prepared for pathological ***:By monitoring ERG as a functional index for the retina,intravitreal injection of 4mg TA showed no significant ERG *** doses of 8,16 and 32,hyper-abnormal responses in a-and b-waves of ERG were detected on the 3rd post-injection *** changes gradually returned back to normal limits *** examination of the retina of all animals showed no pathological ***:High doses of intravitreal TA seemed to have enhancing effects on the retinal function with gradual return to normal limits with no pathological changes detected in examined eyes.

关键词： triamcinolone acetonide intravitreal injection electroretinogram

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Cancelable Speaker Identification System Based on Optical-Like Encryption Algorithms

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Computer Systems Science & Engineering 2022年第10期43卷 87-102页

作者： Safaa el-Gazar Walid el-Shafai ghada el-banby Hesham F.A.Hamed Gerges m.Salama mohammed Abd-elnaby Fathi E.Abd el-Samie Department of electrical Engineering Faculty of EngineeringEgyptian-Russian UniversityCairoEgypt Department of electronics and electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Department of Industrial electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Biometric authentication is a rapidly growing trend that is gaining increasing attention in the last *** achieves safe access to systems using biometrics instead of the traditional *** utilization of a biometric in its original format makes it usable only ***,a cancelable biometric template should be used,so that it can be replaced when it is *** biometrics aims to enhance the security and privacy of biometric *** encryption is an efficient technique to be used in order to generate cancelable biometric *** this paper,a highly-secure encryption algorithm is proposed to ensure secure biometric data in verification *** considered biometric in this paper is the speech *** speech signal is transformed into its ***,the spectrogram is encrypted using two cascaded optical encryption *** first algorithm is the Optical Scanning Holography(OSH)for its efficiency as an encryption *** OSH encrypted spectrogram is encrypted using Double Random Phase Encoding(DRPE)by implementing two Random Phase masks(RPms).After the two cascaded optical encryption algorithms,the cancelable template is *** verification is implemented through correlation estimation between enrolled and test templates in their encrypted *** the correlation value is larger than a threshold value,the user is *** threshold value can be determined from the genuine and imposter correlation distribution curves as the midpoint between the two *** implementation of optical encryption is adopted using its software rather than the optical *** efficiency of the proposed cancelable biometric algorithm is illustrated by the simulation *** can improve the biometric data security without deteriorating the recognition *** results give close-to-zero This values for the Equal Error Rate(EER)and close-to-one values for the Area under Receiver Operator Charact

关键词： Cancelable biometrics spectrogram OSH DRPE EER AROC

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metaheuristic Optimization Through Deep Learning Classification of COVID-19 in Chest X-Ray Images

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Computers, materials & Continua 2022年第11期73卷 4193-4210页

作者： Nagwan Abdel Samee el-Sayed m.el-Kenawy ghada Atteia mona m.Jamjoom Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Noha E.el-Attar Tarek Gaber Adam Slowik mahmoud Y.Shams Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Department of Communications and electronics Delta Higher Institute of Engineering and TechnologyMansoura 35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura 35712Egypt Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura University35516MansouraEgypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams University11566CairoEgypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Faculty of Computers and Artificial Intelligence Benha UniversityEgypt School of Science Engineeringand EnvironmentUniversity of SalfordUK Faculty of Computers and Informatics Suez Canal UniversityIsmailia41522Egypt Koszalin University of Technology Poland Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33511Egypt

As corona virus disease(COVID-19)is still an ongoing global outbreak,countries around the world continue to take precautions and measures to control the spread of the *** of the excessive number of infected patients and the resulting deficiency of testing kits in hospitals,a rapid,reliable,and automatic detection of COVID-19 is in extreme need to curb the number of *** analyzing the COVID-19 chest X-ray images,a novel metaheuristic approach is proposed based on hybrid dipper throated and particle swarm *** lung region was segmented from the original chest X-ray images and augmented using various transformation ***,the augmented images were fed into the VGG19 deep network for feature *** the other hand,a feature selection method is proposed to select the most significant features that can boost the classification ***,the selected features were input into an optimized neural network for *** neural network is optimized using the proposed hybrid *** experimental results showed that the proposed method achieved 99.88%accuracy,outperforming the existing COVID-19 detection *** addition,a deep statistical analysis is performed to study the performance and stability of the proposed *** results confirm the effectiveness and superiority of the proposed approach.

关键词： Covid-19 feature selection dipper throated optimization particle swarm optimization deep learning

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Adipose tissue-derived mesenchymal stem cells and chitosan/poly(vinyl alcohol)nanofibrous scaffolds for cartilage tissue engineering

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Cell Regeneration 2020年第1期9卷 62-73页

作者： ghada Nour-eldeen mazen Abdel-Rasheed Amira m.el-Rafei Osama Azmy Gehan T.el-Bassyouni molecular Genetics and Enzymology Department National Research CentreDokkiCairo 12622Egypt Stem Cell Research group Medical Research Centre of ExcellenceNational Research CentreCairo 12622Egypt Reproductive Health Research Department National Research Centre33 El-Buhouth StDokkiCairo 12622Egypt Refractories Ceramics and Building Materials DepartmentNational Research CentreDokkiCairo 12622Egypt

Osteoarthritis(OA)has been defined as a chronic inflammatory joint disease characterized by progressive articular cartilage *** growing interest in regenerative medicine,using cell therapy and tissue engineering,where cellular components in combination with engineered scaffolds and bioactive materials were used to induce functional tissue *** the present study,nanofibrous scaffold based on chitosan(CS)/poly(vinyl alcohol)(PVA)were used to develop biologically functionalized biomaterial to mimic the extracellular matrix,allowing the human adipose tissue derived mesenchymal stem cells(ADSCs)to proliferate and differentiate to chondrogenic *** morphology of the nanofibrous mat was examined using field emission scanning electron microscope(FE/SEm).The characteristic functional groups and the nature of the chemical bonds between atoms were evaluated using Fourier transform infrared spectroscopy(FTIR)*** of the seeded cells was morphologically evaluated by scanning electron microscopy and by flow cytometry for the expression of the stem cell surface *** differentiation potential was verified after chondrogenic induction by analyzing the expression of chondrogenic marker genes using real-time(RT PCR).Current study suggest significant potential for the use of ADSCs with the nanofibrous scaffolds in improving the osteoarthritis pathology.

关键词： Adipose tissue mesenchymal stem cells Nanofibrous scaffolds Osteoarthritis Cartilage tissue engineering

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