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Automated COVID-19 Detection Based on Single-Image Super-Resolution and CNN models

Computers, materials & Continua 2022年第1期70卷 1141-1157页

作者： Walid el-Shafai Anas m.Ali el-sayed m.el-rabaie Naglaa F.Soliman Abeer D.Algarni Fathi E.Abd el-Samie Department electronics and electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Alexandria Higher Institute of Engineering&Technology(AIET) AlexandriaEgypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia

In developing countries,medical diagnosis is expensive and time ***,automatic diagnosis can be a good cheap *** task can be performed with artificial intelligence tools such as deep Convolutional Neural Networks(CNNs).These tools can be used on medical images to speed up the diagnosis process and save the efforts of *** deep CNNs allow direct learning from the medical ***,the accessibility of classified data is still the largest challenge,particularly in the field of medical *** learning can deliver an effective and promising solution by transferring knowledge from universal object detection CNNs to medical image ***,because of the inhomogeneity and enormous overlap in intensity between medical images in terms of features in the diagnosis of Pneumonia and COVID-19,transfer learning is not usually a robust ***-Image Super-Resolution(SISR)can facilitate learning to enhance computer vision functions,apart from enhancing perceptual image ***,it helps in showing the main features of *** by the challenging dilemma of Pneumonia and COVID-19 diagnosis,this paper introduces a hybrid CNN model,namely SIGTra,to generate super-resolution versions of X-ray and CT *** depends on aGenerative Adversarial Network(GAN)for the super-resolution reconstruction ***,Transfer learning with CNN(TCNN)is adopted for the classification of *** different categories of chest X-ray and CT images can be classified with the proposed model.A comparison study is presented between the proposed SIGTra model and the other relatedCNNmodels for COVID-19 detection in terms of precision,sensitivity,and accuracy.

关键词： medical images SIGTra GAN CT and X-ray images SISR TCNN

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Efficient Deep CNN model for COVID-19 Classification

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Computers, materials & Continua 2022年第3期70卷 4373-4391页

作者： Walid el-Shafai Amira A.mahmoud el-sayed m.el-rabaie Taha E.Taha Osama F.Zahran Adel S.el-Fishawy mohammed Abd-elnaby Fathi E.Abd el-Samie Department electronics and electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia 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

Coronavirus(COVID-19)infection was initially acknowledged as a global pandemic in Wuhan in *** Health Organization(WHO)stated that the COVID-19 is an epidemic that causes a 3.4%death *** X-Ray(CXR)and Computerized Tomography(CT)screening of infected persons are essential in diagnosis *** are numerous ways to identify positive COVID-19 *** of the fundamental ways is radiology imaging through CXR,or CT *** comparison of CT and CXR scans revealed that CT scans are more effective in the diagnosis process due to their high ***,automated classification techniques are required to facilitate the diagnosis *** Learning(DL)is an effective tool that can be utilized for detection and classification this type of medical *** deep Convolutional Neural Networks(CNNs)can learn and extract essential features from different medical image *** this paper,a CNN architecture for automated COVID-19 detection from CXR and CT images is *** activation functions as well as three optimizers are tested and compared for this *** proposed architecture is built from scratch and the COVID-19 image datasets are directly fed to train *** performance is tested and investigated on the CT and CXR *** activation functions:Tanh,Sigmoid,and RelU are compared using a constant learning rate and different batch *** optimizers are studied with different batch sizes and a constant learning ***,a comparison between different combinations of activation functions and optimizers is presented,and the optimal configuration is ***,the main objective is to improve the detection accuracy of COVID-19 from CXR and CT images using DL by employing CNNs to classify medical COVID-19 images in an early *** proposed model achieves a classification accuracy of 91.67%on CXR image dataset,and a classification accuracy of 100%on CT dataset with training times of 58 min and 46 min on CXR an

关键词： COVID-19 image classification CNN DL activation functions optimizers

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Efficient Segmentation Approach for Different medical Image modalities

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Computers, materials & Continua 2022年第11期73卷 3119-3135页

作者： Walid el-Shafai Amira A.mahmoud el-sayed m.el-rabaie Taha E.Taha Osama F.Zahran Adel S.el-Fishawy Naglaa F.Soliman Amel A.Alhussan Fathi E.Abd el-Samie Department electronics and electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia

This paper presents a study of the segmentation of medical *** paper provides a solid introduction to image enhancement along with image segmentation *** the first step,the morphological operations are employed to ensure image detail protection and *** objective of using morphological operations is to remove the defects in the texture of the ***,the Fuzzy C-means(FCm)clustering algorithm is used to modify membership function based only on the spatial neighbors instead of the distance between pixels within local spatial neighbors and cluster *** proposed technique is very simple to implement and significantly fast since it is not necessary to compute the distance between the neighboring pixels and the cluster *** is also efficient when dealing with noisy images because of its ability to efficiently improve the membership partition *** results are performed on different medical image ***(Us),X-ray(mammogram),Computed Tomography(CT),Positron Emission Tomography(PET),and magnetic Resonance(mR)images are the main medical image modalities used in this *** obtained results illustrate that the proposed technique can achieve good results with a short time and efficient image *** results on different image modalities show that the proposed technique can achieve segmentation accuracies of 98.83%,99.71%,99.83%,99.85%,and 99.74%for Us,mammogram,CT,PET,and mRI images,respectively.

关键词： Image segmentation ultrasonic mammogram CT PET mRI morphological operations FCm active contours

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An Improved Convolutional Neural Network model for DNA Classification

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Computers, materials & Continua 2022年第3期70卷 5907-5927页

作者： Naglaa.F.Soliman Samia m.Abd-Alhalem Walid el-Shafai Salah eldin S.E.Abdulrahman N.Ismaiel el-sayed m.el-rabaie Abeer D.Algarni Fathi E.Abd el-Samie Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia Department of electronics and electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenoufia32952Egypt Department of Computer Science and Engineering Faculty of Electronic EngineeringMenoufia UniversityMenoufia32952Egypt

Recently,deep learning(DL)became one of the essential tools in bioinformatics.A modified convolutional neural network(CNN)is employed in this paper for building an integratedmodel for deoxyribonucleic acid(DNA)*** any CNN model,convolutional layers are used to extract features followed by max-pooling layers to reduce the dimensionality of features.A novel method based on downsampling and CNNs is introduced for feature *** downsampling is an improved form of the existing pooling layer to obtain better classification *** two-dimensional discrete transform(2D DT)and two-dimensional random projection(2D RP)methods are applied for *** convert the high-dimensional data to low-dimensional data and transform the data to the most significant feature ***,there are parameters which directly affect how a CNN model is *** this paper,some issues concerned with the training of CNNs have been *** CNNs are examined by changing some hyperparameters such as the learning rate,size of minibatch,and the number of *** and assessment of the performance of CNNs are carried out on 16S rRNA bacterial *** results indicate that the utilization of a CNN based on wavelet subsampling yields the best trade-off between processing time and accuracy with a learning rate equal to 0.0001,a size of minibatch equal to 64,and a number of epochs equal to 20.

关键词： DNA classification CNN downsampling hyperparameters DL 2D DT 2D RP

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Hybrid Segmentation Approach for Different medical Image modalities

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Computers, materials & Continua 2022年第11期73卷 3454-3471页

作者： Walid el-Shafai Amira A.mahmoud el-sayed m.el-rabaie Taha E.Taha Osama F.Zahran Adel S.el-Fishawy Naglaa F.Soliman Amel A.Alhussan Fathi E.Abd el-Samie Department electronics and electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia

The segmentation process requires separating the image region into sub-regions of similar *** sub-region has a group of pixels having the same characteristics,such as texture or *** paper suggests an efficient hybrid segmentation approach for different medical image modalities based on particle swarm optimization(PSO)and improved fast fuzzy C-means clustering(IFFCm)*** extensive comparative study on different medical images is presented between the proposed approach and other different previous segmentation *** existing medical image segmentation techniques incorporate clustering,thresholding,graph-based,edge-based,active contour,region-based,and watershed *** paper extensively analyzes and summarizes the comparative investigation of these ***,a prediction of the improvement involves the combination of these techniques is *** obtained results demonstrate that the proposed hybrid medical image segmentation approach provides superior outcomes in terms of the examined evaluation metrics compared to the preceding segmentation techniques.

关键词： Image segmentation ultrasonic images X-ray images CT images PET images mR images fuzzy c-mean morphological operations active contour

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Deep CNN model for multimodal medical Image Denoising

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Computers, materials & Continua 2022年第11期73卷 3795-3814页

作者： Walid el-Shafai Amira A.mahmoud Anas m.Ali el-sayed m.el-rabaie Taha E.Taha Osama F.Zahran Adel S.el-Fishawy Naglaa F.Soliman Amel A.Alhussan Fathi E.Abd el-Samie Department electronics and electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Alexandria Higher Institute of Engineering&Technology(AIET) AlexandriaEgypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia

In the literature,numerous techniques have been employed to decrease noise in medical image modalities,including X-Ray(XR),Ultrasonic(Us),Computed Tomography(CT),magnetic Resonance Imaging(mRI),and Positron Emission Tomography(PET).These techniques are organized into two main classes:the multiple Image(mI)and the Single Image(SI)*** the mI techniques,images usually obtained for the same area scanned from different points of view are used.A single image is used in the entire procedure in the SI *** denoising techniques can be carried out both in a transform or spatial *** paper is concerned with single-image noise reduction techniques because we deal with single medical *** most well-known spatial domain noise reduction techniques,including Gaussian filter,Kuan filter,Frost filter,Lee filter,Gabor filter,median filter,Homomorphic filter,Speckle reducing anisotropic diffusion(SRAD),Nonlocal-means(NL-means),and Total Variation(TV),are ***,the transform domain noise reduction techniques,including wavelet-based and Curvelet-based techniques,and some hybridization techniques are ***,a deep(Convolutional Neural Network)CNN-based denoising model is proposed to eliminate Gaussian and Speckle noises in different medical image *** model utilizes the Batch Normalization(BN)and the RelU as a basic *** a result,it attained a considerable improvement over the traditional *** previously mentioned techniques are evaluated and compared by calculating qualitative visual inspection and quantitative parameters like Peak Signal-to-Noise Ratio(PSNR),Correlation Coefficient(Cr),and system complexity to determine the optimum denoising algorithm to be applied *** on the quality metrics,it is demonstrated that the proposed deep CNN-based denoising model is efficient and has superior denoising performance over the traditionaldenoising techniques.

关键词： Image enhancement medical imaging speckle noise Gaussian noise denoising filters CNN denoising

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High-Sensitivity Early Detection Biomedical Sensor for Tuberculosis With Low Losses in the Terahertz Regime Based on Photonic Crystal Fiber Technology

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Photonic Sensors 2023年第2期13卷 14-29页

作者： Nazmi A.mOHAmmED Omar E.KHEDR el-sayed m.el-rabaie Ashraf A.m.KHALAF Department of electronics and Communication Engineering Medina Higher Institute for Engineering and TechnologyGiza 12947Egypt Department of Communications and electronics Alexandria Higher Institute for Engineering and TechnologyAlexandria 21311Egypt Department of electronics and Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf 32952Egypt Department of Communications and electronics Faculty of EngineeringMinia UniversityMinia 61111Egypt

Tuberculosis is one of the most contagious and lethal illnesses in the world,according to the World Health *** had the leading mortality rate as a result of a single infection,ranking above HIV/*** detection is an essential factor in patient treatment and can improve the survival *** methods should have high mobility,high accuracy,fast detection,and low *** work presents a novel biomedical photonic crystal fiber sensor,which can accurately detect and distinguish between the different types of tuberculosis *** designed sensor detects these types with high relative sensitivity and negligible losses compared to other photonic crystal fiber-based biomedical *** 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-f,and a numerical aperture of *** proposed sensor is capable of operating in the terahertz regimes over a wide range(1 THz-2.4 THz).An abbreviated review of non-optical detection techniques is also *** 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.

关键词： Tuberculosis photonic crystal fiber relative sensitivity terahertz confinement loss effective area

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A novel hybrid cryptosystem based on DQFrFT watermarking and 3D-CLm encryption for healthcare services

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Frontiers of Information Technology & electronic Engineering 2023年第7期24卷 1045-1061页

作者： Fatma KHALLAF Walid el-SHAFAI el-sayed m.el-rabaie Naglaa F.SOLImAN Fathi EAbd E.L-SAmIE Department of electronics and electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf 32952Egypt Department of electrical Engineering Faculty of EngineeringAhram Canadian University6^(th)October CityGiza 12451Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh I1586Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia

Quaternion algebra has been used to apply the fractional Fourier transform(FrFT)to color images in a comprehensive ***,the discrete fractional random transform(DFRNT)with adequate basic randomness remains to be *** paper presents a novel multistage privacy system for color medical images based on discrete quaternion fractional Fourier transform(DQFrFT)watermarking and three-dimensional chaotic logistic map(3D-CLm)***,we describe quaternion DFRNT(QDFRNT),which generalizes DFRNT to handle quaternion signals effectively,and then use QDFRNT to perform color medical image adaptive *** efficiently evaluate QDFRNT,this study derives the relationship between the QDFRNT of a quaternion signal and the four components of the DFRNT ***,it uses the human vision system's(HVS)masking qualities of edge,texture,and color tone immediately from the color host image to adaptively modify the watermark strength for each block in the color medical image using the QDFRNT-based adaptive watermarking and support vector machine(SVm)*** limitations of watermark embedding are also explained to conserve watermarking ***,3D-CLm encryption is employed to improve the system's security and efficiency,allowing it to be used as a multistage privacy *** proposed security system is effective against many types of channel noise attacks,according to simulation results.

关键词： Color medical image Quaternion Adaptive watermarking Encryption Fractional transform Three-dimensional chaotic logistic map(3D-CLm)

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Classical taxonomy,molecular phylogeny and genetic analysis of the genus Exitianus Ball,1929(Hemiptera:Cicadellidae:Deltocephalinae)from Egypt

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Zoological Systematics 2024年第2期49卷 143-157页

作者： Rabab m.el-Hady Ahmed F.el-sayed Survey&Taxonomy Department Plant Protection Research InstituteAgriculture Research CentreGizaEgypt microbial Genetics Department Biotechnology Research InstituteNational Research CentreGizaEgypt Egypt Center for Research and Regenerative medicine(ECRRm) CairoEgypt

Egyptian species of the leafhopper genus Exitianus Ball,1929,***(St?l,1855),***(Distant,1908),and *** Ross,1968 are ***,morphological descriptions,and a key for their identification are *** this study,we used molecular techniques to confirm morphological identification and detect phylogeny among the three Exitianus *** partial nucleotide sequences of the amplified products obtained were determined by macrogen *** nucleotide sequences of 28S rDNA and COX genes of the EGY-ARC-9,EGY-ARC-4,and EGY-ARC-5 were determined by macrogen Korea,blasted into BLAST at the National Center for Biotechnology Information website(NCBI)and compared with those deposited in the GenBank DNA *** results represented the homology percentage between the partial sequences of the 28S rRNA and COX genes from each species and related species obtained from GenBank DNA *** morphological identifications of the three Exitianus species were confirmed by molecular characterization and sequencing of 28S rDNA and COX genes and identified as ***,***,and ***,their sequences of 28srDNA and COX genes were deposited in GenBank with an accession number(LC670610,LC670607,and OQ196105)for 28srDNA gene and(LC775357,LC775358,and LC775359)for the COX gene.

关键词： Auchenorrhyncha Chaismini taxonomic 28S rDNA phylogenetic

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Urban electric Vehicle Charging Station Placement Optimization with Graylag Goose Optimization Voting Classifier

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Computers, materials & Continua 2024年第7期80卷 1163-1177页

作者： Amel Ali Alhussan Doaa Sami Khafaga el-sayed m.el-kenawy marwa m.Eid Abdelhameed Ibrahim Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and electronics Delta Higher Institute for Engineering and TechnologyMansoura35511Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt

To reduce the negative effects that conventional modes of transportation have on the environment,researchers are working to increase the use of electric *** demand for environmentally friendly transportation may be hampered by obstacles such as a restricted range and extended rates of *** establishment of urban charging infrastructure that includes both fast and ultra-fast terminals is essential to address this ***,the powering of these terminals presents challenges because of the high energy requirements,whichmay influence the quality of *** the maximum hourly capacity of each station based on its geographic location is necessary to arrive at an accurate estimation of the resources required for charging *** is vital to do an analysis of specific regional traffic patterns,such as road networks,route details,junction density,and economic zones,rather than making arbitrary conclusions about traffic *** vehicle traffic is simulated using this data and other variables,it is possible to detect limits in the design of the current traffic engineering ***,the binary graylag goose optimization(bGGO)algorithm is utilized for the purpose of feature ***,the graylag goose optimization(GGO)algorithm is utilized as a voting classifier as a decision algorithm to allocate demand to charging stations while taking into consideration the cost variable of traffic *** on the results of the analysis of variance(ANOVA),a comprehensive summary of the components that contribute to the observed variability in the dataset is *** results of the Wilcoxon Signed Rank Test compare the actual median accuracy values of several different algorithms,such as the voting GGO algorithm,the voting grey wolf optimization algorithm(GWO),the voting whale optimization algorithm(WOA),the voting particle swarm optimization(PSO),the voting firefly algorithm(FA),and the voting genetic algori

关键词： electric vehicle graylag goose optimization metaheuristics optimization machine learning

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