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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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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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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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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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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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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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electro-optic tuning in composite silicon photonics based on ferroionic 2D materials

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Light(Science & Applications) 2024年第5期13卷 852-864页

作者： ghada Dushaq Solomon Serunjogi Srinivasa R.Tamalampudi mahmoud Rasras Department of electrical and Computer Engineering New York University Abu DhabiP.O.Box 129188Abu DhabiUnited Arab Emirates NYU Tandon School of Engineering New York UniversityNew YorkNYUSA

Tunable optical materials are indispensable elements in modern optoelectronics,especially in integrated photonics circuits where precise control over the effective refractive index is essential for diverse applications.Two-dimensional materials like transition metal dichalcogenides(TmDs)and graphene exhibit remarkable optical responses to external stimuli.However,achieving distinctive modulation across short-wave infrared(SWIR)regions while enabling precise phase control at low signal loss within a compact footprint remains an ongoing challenge.In this work,we unveil the robust electro-refractive response of multilayer ferroionic two-dimensional CuCrP2S6(CCPS)in the near-infrared wavelength range.By integrating CCPS into silicon photonics(SiPh)microring resonators(mRR),we enhance lightmatter interaction and measurement sensitivity to minute phase and absorption variations.Results show that electrically driven Cu ions can tune the effective refractive index on the order of 2.8×10−3 RIU(refractive index unit)while preserving extinction ratios and resonance linewidth.Notably,these devices exhibit low optical losses and excellent modulation efficiency of 0.25 V.cm with a consistent blue shift in the resonance wavelengths among all devices for either polarity of the applied voltage.These results outperform earlier findings on phase shifters based on TmDs.Furthermore,our study demonstrates distinct variations in electro-optic tuning sensitivity when comparing transverse electric(TE)and transverse magnetic(Tm)modes,revealing a polarization-dependent response that paves the way for diverse applications in light manipulation.The combined optoelectronic and ionotronic capabilities of two-terminal CCPS devices present extensive opportunities across several domains.Their potential applications range from phased arrays and optical switching to their use in environmental sensing and metrology,optical imaging systems,and neuromorphic systems in light-sensitive artificial synapses.

关键词： tuning polarization refractive

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Shear Let Transform Residual Learning Approach for Single-Image Super-Resolution

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Computers, materials & Continua 2024年第5期79卷 3193-3209页

作者： Israa Ismail ghada eltaweel mohamed meselhy eltoukhy Department of Computer Science Faculty of Computers and InformaticsSuez Canal UniversityIsmailia51422Egypt Department of Information Technology College of Computing and Information Technology at KhulaisUniversity of JeddahJeddahSaudi Arabia

Super-resolution techniques are employed to enhance image resolution by reconstructing high-resolution images from one or more low-resolution inputs.Super-resolution is of paramount importance in the context of remote sensing,satellite,aerial,security and surveillance imaging.Super-resolution remote sensing imagery is essential for surveillance and security purposes,enabling authorities to monitor remote or sensitive areas with greater clarity.This study introduces a single-image super-resolution approach for remote sensing images,utilizing deep shearlet residual learning in the shearlet transform domain,and incorporating the Enhanced Deep Super-Resolution network(EDSR).Unlike conventional approaches that estimate residuals between high and low-resolution images,the proposed approach calculates the shearlet coefficients for the desired high-resolution image using the provided low-resolution image instead of estimating a residual image between the high-and low-resolution image.The shearlet transform is chosen for its excellent sparse approximation capabilities.Initially,remote sensing images are transformed into the shearlet domain,which divides the input image into low and high frequencies.The shearlet coefficients are fed into the EDSR network.The high-resolution image is subsequently reconstructed using the inverse shearlet transform.The incorporation of the EDSR network enhances training stability,leading to improved generated images.The experimental results from the Deep Shearlet Residual Learning approach demonstrate its superior performance in remote sensing image recovery,effectively restoring both global topology and local edge detail information,thereby enhancing image quality.Compared to other networks,our proposed approach outperforms the state-of-the-art in terms of image quality,achieving an average peak signal-to-noise ratio of 35 and a structural similarity index measure of approximately 0.9.

关键词： Super-resolution shearlet transform shearlet coefficients enhanced deep super-resolution network

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Particle Swarm Optimization-Based Hyperparameters Tuning of machine Learning models for Big COVID-19 Data Analysis

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Journal of Computer and Communications 2024年第3期12卷 160-183页

作者： Hend S. Salem mohamed A. mead ghada S. el-Taweel Department of Computer Sciences Suez Canal University Ismailia Egypt

Analyzing big data, especially medical data, helps to provide good health care to patients and face the risks of death. The COVID-19 pandemic has had a significant impact on public health worldwide, emphasizing the need for effective risk prediction models. machine learning (mL) techniques have shown promise in analyzing complex data patterns and predicting disease outcomes. The accuracy of these techniques is greatly affected by changing their parameters. Hyperparameter optimization plays a crucial role in improving model performance. In this work, the Particle Swarm Optimization (PSO) algorithm was used to effectively search the hyperparameter space and improve the predictive power of the machine learning models by identifying the optimal hyperparameters that can provide the highest accuracy. A dataset with a variety of clinical and epidemiological characteristics linked to COVID-19 cases was used in this study. Various machine learning models, including Random Forests, Decision Trees, Support Vector machines, and Neural Networks, were utilized to capture the complex relationships present in the data. To evaluate the predictive performance of the models, the accuracy metric was employed. The experimental findings showed that the suggested method of estimating COVID-19 risk is effective. When compared to baseline models, the optimized machine learning models performed better and produced better results.

关键词： Big COVID-19 Data machine Learning Hyperparameter Optimization Particle Swarm Optimization Computational Intelligence

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Systemic oncological therapy in breast cancer patients on dialysis

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World Journal of Clinical Oncology 2024年第6期15卷 730-744页

作者： Salman Khan ghada Araji Ekrem Yetiskul Praneeth Reddy Keesari Fadi Haddadin Zaid Khamis Varun Chowdhry muhammad Niazi Sarah Afif meekoo Dhar Suzanne el-Sayegh Department of Internal medicine Northwell Health-Staten Island University HospitalStaten IslandNY 10305United States Department of Hematology and Oncology Northwell Health-Staten Island University HospitalStaten IslandNY 10305United States Department of Internal medicine CUNY School of MedicineNew YorkNY 10031United States

The advancement of renal replacement therapy has significantly enhanced the survival rates of patients with end-stage renal disease(ESRD)over time.How-ever,this prolonged survival has also been associated with a higher likelihood of cancer diagnoses among these patients including breast cancer.Breast cancer treatment typically involves surgery,radiation,and systemic therapies,with ap-proaches tailored to cancer type,stage,and patient preferences.However,renal replacement therapy complicates systemic therapy due to altered drug clearance and the necessity for dialysis sessions.This review emphasizes the need for opti-mized dosing and administration strategies for systemic breast cancer treatments in dialysis patients,aiming to ensure both efficacy and safety.Additionally,ch-allenges in breast cancer screening and diagnosis in this population,including soft-tissue calcifications,are highlighted.

关键词： Breast cancer Systemic therapy Renal replacement therapy Dialysis End-stage renal disease Hormone therapy Chemotherapy

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