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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 world.It has significantly influenced public health,the world economy,and the persons’lives.Hence,there is a need to speed up the diagnosis and precautions to deal with COVID-19 patients.With this explosion of this pandemic,there is a need for automated diagnosis tools to help specialists based onmedical images.This paper presents a hybrid Convolutional Neural Network(CNN)-based classification and segmentation approach for COVID-19 detection from Computed Tomography(CT)images.The proposed approach is employed to classify and segment the COVID-19,pneumonia,and normal CT images.The classification stage is firstly applied to detect and classify the input medical CT images.Then,the segmentation stage is performed to distinguish between pneumonia and COVID-19 CT images.The classification stage is implemented based on a simple and efficient CNN deep learning model.This model comprises four Rectified Linear Units(RelUs),four batch normalization layers,and four convolutional(Conv)layers.TheConv 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 process.For the segmentation process,the Simplified Pulse Coupled Neural Network(SPCNN)is utilized in the proposed hybrid approach.The proposed segmentation approach is based on salient object detection to localize the COVID-19 or pneumonia region,accurately.To 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 system.Once 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 images.The region of interest can be assesses both automatically and through experts.This strategy helps somuch in saving the time and ef

关键词： COVID-19 segmentation classification CNN SPCNN CT 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 frames.Computer vision is a visual search trend that is used to identify objects in images or video frames.For military applications,drones take a main role in surveillance tasks,but they cannot be confident for longtime missions.So,there is a need for such a system,which provides a continuous surveillance task to support the drone mission.Such a system can be called a Hybrid Surveillance System(HSS).This system is based on a distributed network of wireless sensors for continuous surveillance.In addition,it includes one or more drones to make short-time missions,if the sensors detect a suspicious event.This 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 streams.The accuracy of the proposed methods reach 99%,which reflects the effectiveness of the presented solution.In 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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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 sources.It is involved in several applications.One of the recent applications is the robotic vision.This application necessitates image enhancement of both infrared(IR)and visible images.This paper presents a Robot Human Interaction System(RHIS)based on image fusion and deep learning.The basic objective of this system is to fuse visual and IR images for efficient feature extraction from the captured images.Then,an enhancement model is carried out on the fused image to increase its quality.Several 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 works.Simulation results prove that the fuzzy logic enhancement gives the best results from the image quality perspective.Hence,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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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)networks.The 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 applications.The original biometric is blurred with two co-prime operators.Hence,it can be recovered as the Greatest Common Divisor(GCD)between its two blurred versions.Minimal 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 operation.Hence,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 image.Furthermore,we test the utility of the proposed algorithm in the single-and multi-biometric scenarios.The multi-biometric scenario depends on compressing face,fingerprint,iris,and palm print images,simultaneously,to generate the cancelable templates.Evaluation metrics such as Equal Error Rate(EER)and Area and Receiver Operator Characteristic curve(AROC)are considered.Simulation 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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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 decades.It achieves safe access to systems using biometrics instead of the traditional passwords.The utilization of a biometric in its original format makes it usable only once.Therefore,a cancelable biometric template should be used,so that it can be replaced when it is attacked.Cancelable biometrics aims to enhance the security and privacy of biometric authentication.Digital encryption is an efficient technique to be used in order to generate cancelable biometric templates.In this paper,a highly-secure encryption algorithm is proposed to ensure secure biometric data in verification systems.The considered biometric in this paper is the speech signal.The speech signal is transformed into its spectrogram.Then,the spectrogram is encrypted using two cascaded optical encryption algorithms.The first algorithm is the Optical Scanning Holography(OSH)for its efficiency as an encryption tool.The 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 obtained.The verification is implemented through correlation estimation between enrolled and test templates in their encrypted format.If the correlation value is larger than a threshold value,the user is authorized.The threshold value can be determined from the genuine and imposter correlation distribution curves as the midpoint between the two curves.The implementation of optical encryption is adopted using its software rather than the optical setup.The efficiency of the proposed cancelable biometric algorithm is illustrated by the simulation results.It can improve the biometric data security without deteriorating the recognition accuracy.Simulation results give close-to-zero This values for the Equal Error Rate(EER)and close-to-one values for the Area under Receiver Operator Characteristic(AROC)curve.

关键词： Cancelable biometrics spectrogram OSH DRPE EER AROC

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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 world.It has dramatically affected daily lives,public health,and the world economy.This paper presents a segmentation and classification framework of COVID-19 images based on deep learning.Firstly,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 images.Finally,the resulting segmented images are used to identify the infected region,whether COVID-19 or pneumonia.The 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 64.Four maxpooling layers are employed with a stride of 2 and a 2×2 window.The 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 images.The 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 objects.However,its output has a low resolution,and it needs more processing for more interpretation.This paper presents two algorithms for landmine detection from GPR images.The 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 gradients.Then,histogram and cumulative histogram are estimated for the overall gradient image.The bin values on the cumulative histogram are used for discrimination between images with and without landmines.Moreover,a neural classifier is used to classify images with cumulative histograms as feature vectors.The second algorithm is based on scale-space analysis with the number of speeded-up robust feature(SURF)points as the key parameter for classification.In addition,this paper presents a framework for size reduction of GPR images based on decimation for efficient storage.The further classification steps can be performed on images after interpolation.The 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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Latent Space Representational Learning of Deep Features for Acute Lymphoblastic Leukemia Diagnosis

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Computer Systems Science & Engineering 2023年第4期45卷 361-376页

作者： ghada Emam Atteia Information Technology Department College of Computer&Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11461Saudi Arabia

Acute Lymphoblastic Leukemia(ALL)is a fatal malignancy that is featured by the abnormal increase of immature lymphocytes in blood or bone marrow.Early prognosis of ALL is indispensable for the effectual remediation of this disease.Initial screening of ALL is conducted through manual examination of stained blood smear microscopic images,a process which is time-consuming and prone to errors.Therefore,many deep learning-based computer-aided diagnosis(CAD)systems have been established to automatically diagnose ALL.This paper proposes a novel hybrid deep learning system for ALL diagnosis in blood smear images.The introduced system integrates the proficiency of autoencoder networks in feature representational learning in latent space with the superior feature extraction capability of standard pretrained convolutional neural networks(CNNs)to identify the existence of ALL in blood smears.An augmented set of deep image features are formed from the features extracted by GoogleNet and Inception-v3 CNNs from a hybrid dataset of microscopic blood smear images.A sparse autoencoder network is designed to create an abstract set of significant latent features from the enlarged image feature set.The latent features are used to perform image classification using Support Vector Machine(SVM)classifier.The obtained results show that the latent features improve the classification performance of the proposed ALL diagnosis system over the original image features.Moreover,the classification performance of the system with various sizes of the latent feature set is evaluated.The retrieved results reveal that the introduced ALL diagnosis system superiorly compete the state of the art.

关键词： Autoencoder deep learning CNN leukemia diagnosis computeraided diagnosis

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Comparison of sucrose intake and production of elimination spots among adult Musca domestica, Musca autumnalis, Phormia regina and Protophormia terraenovae

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亚太热带生物医学杂志：英文版 2016年第8期6卷 640-645页

作者： ghada Mohamed el-Bassiony John George Stoffolano Jr Department of Entomology Faculty of Science Cairo University Stockbridge School of Agriculture University of Massachusetts

Objective: To compare the differences in intake and excretion between Musca domestica and other three species from families Muscidae and Calliphoridae which may help explaining the significance of house fly in the transmission of pathogens.Methods: The four adult species were supplied with two concentrations of sucrose via modified capillary feeder assay system. The two sucrose concentrations were applied to one adult male/each experiment and the elimination spots were counted. Using 0.25 mol/L sucrose + 0.25% bromophenol blue, one active non-starved male/cup was observed carefully for 1 h to record its behavior. As a growing medium used in bacterial transmission experiments, undiluted trypticase soy broth was used to feed 3-day-old females and males of Musca domestica following two different diets upon emergence and the frequency of elimination spots was estimated.Results: The two Musca species have half the weight of the two Phormia species.Comparing the volume of intake per hour, house fly took as much as the other species, all of which were larger. House fly produced twice, or more, the number of elimination spots/h than the other three species. Feeding the flies a sugar liquid diet resulted in producing more fecal spots than regurgitation spots. The male house flies produced less elimination spots/h when fed with trypticase soy broth than with the two sucrose solutions.Conclusions: House flies eliminated more than the other examined fly species and most of these elimination events were defecation which implicates the fecal route for pathogen transmission by this important vector.

关键词： Regurgitation Defecation Non-hematophagous Diptera Pathogen transmission Food safety

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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 rabbits.METHODS: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 saline.In groups2,3,4 and 5,each rabbit received a single intravitreal injection of 4,8,16 and 32mg of TA,respectively.Each dose was contained in 0.1 mL phosphate buffered saline.Clinical 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 days.After17d,animals were sacrificed and their eyes prepared for pathological examination.RESULTS:By monitoring ERG as a functional index for the retina,intravitreal injection of 4mg TA showed no significant ERG changes.At doses of 8,16 and 32,hyper-abnormal responses in a-and b-waves of ERG were detected on the 3rd post-injection day.These changes gradually returned back to normal limits after17d.Histopathological examination of the retina of all animals showed no pathological changes.CONCLUSION: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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