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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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A multi-Stage Security Solution for medical Color Images in Healthcare Applications

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Computer Systems Science & Engineering 2023年第9期46卷 3599-3618页

作者： Walid el-Shafai Fatma Khallaf el-sayed m.el-rabaie Fathi E.Abd el-Samie Iman Almomani Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of electronics and electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of electrical Engineering Faculty of EngineeringAhram Canadian UniversityGizaEgypt Computer Science Department King Abdullah II School of Information TechnologyThe University of JordanJordan11942Jordan

This paper presents a robust multi-stage security solution based on fusion,encryption,and watermarking processes to transmit color healthcare images,*** presented solution depends on the features of discrete cosine transform(DCT),lifting wavelet transform(LWT),and singular value decomposition(SVD).The primary objective of this proposed solution is to ensure robustness for the color medical watermarked images against transmission *** watermark embedding,the host color medical image is transformed into four sub-bands by employing three stages of *** resulting low-frequency sub-band is then transformed by employing three stages of DCT followed by SVD ***,a fusion process is used for combining different watermarks into a single watermark *** single fused image is then ciphered using Deoxyribose Nucleic Acid(DNA)encryption to strengthen the ***,the DNA-ciphered fused watermark is embedded in the host medical image by applying the suggested watermarking technique to obtain the watermarked *** main contribution of this work is embedding multiple watermarks to prevent identity *** the presence of different multimedia attacks,several simulation tests on different colormedical images have been *** results prove that the proposed security solution achieves a decent imperceptibility quality with high Peak Signal-to-Noise Ratio(PSNR)values and high correlation between the extracted and original watermark ***,the watermark image extraction process succeeds in achieving high efficiency in the presence of attacks compared with related works.

关键词： medical images DNA encryption digital image watermarking fusion healthcare applications

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Advanced Functional electromagnetic Shielding materials:A Review Based on micro‑Nano Structure Interface Control of Biomass Cell Walls

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Nano-micro Letters 2025年第1期17卷 98-134页

作者： Yang Shi mingjun Wu Shengbo Ge Jianzhang Li Anoud Saud Alshammari Jing Luo mohammed A.Amin Hua Qiu Jinxuan Jiang Yazeed m.Asiri Runzhou Huang Hua Hou Zeinhom m.el‑Bahy Zhanhu Guo Chong Jia Kaimeng Xu Xiangmeng Chen Co‑Innovation Center of Efficient Processing and Utilization of Forest Resources College of Materials Science and EngineeringNanjing Forestry UniversityNanjing 210037People’s Republic of China State Key Laboratory of Efficient Production of Forest Resourced Beijing Forestry UniversityQinghua East Road 35Haidian DistrictBeijing 100083People’s Republic of China Department of Physics Faculty of Sciences‑ArarNorthern Border UniversityArar 91431Saudi Arabia Department of Chemistry College of ScienceTaif UniversityP.O.Box 1109921944 TaifSaudi Arabia Shaanxi Key Laboratory of macromolecular Science and Technology School of Chemistry and Chemical EngineeringNorthwestern Polytechnical UniversityXi’anShaanxi 710072People’s Republic of China Integrated Composites Lab Department of Mechanical and Construction EngineeringNorthumbria UniversityNewcastle Upon Tyne NE18STUK College of materials Science and Engineering Taiyuan University of Science and TechnologyTaiyuan 030024People’s Republic of China Department of Chemistry Faculty of ScienceAl-Azhar UniversityNasr CityCairo 11884Egypt Yunnan Provincial Key Laboratory of Wood Adhesives and Glued Products International Joint Research Center for Biomass MaterialsSouthwest Forestry UniversityKunming 650224People’s Republic of China School of Science Henan Agricultural UniversityZhengzhou 450002People’s Republic of China

Research efforts on electromagnetic interference(EmI)shielding materials have begun to converge on green and sustainable biomass *** materials offer numerous advantages such as being lightweight,porous,and *** to their porous nature,interfacial compatibility,and electrical conductivity,biomass materials hold significant potential as EmI shielding *** concerted efforts on the EmI shielding of biomass materials have been reported,this research area is still relatively new compared to traditional EmI shielding *** particular,a more comprehensive study and summary of the factors influencing biomass EmI shielding materials including the pore structure adjustment,preparation process,and micro-control would be *** preparation methods and characteristics of wood,bamboo,cellulose and lignin in EmI shielding field are critically discussed in this paper,and similar biomass EmI materials are summarized and *** composite methods and fillers of various biomass materials were *** paper also highlights the mechanism of EmI shielding as well as existing prospects and challenges for development trends in this field.

关键词： Biomass materials electromagnetic interference shielding micro-nano structure interface control Conductivity

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