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Enhancing Early Detection of Lung Cancer through Advanced Image Processing Techniques and Deep Learning Architectures for CT Scans

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Computers, Materials and Continua 2024年第1期81卷 271-307页

作者： Taw.ik N. Emara H.M. el-shafai w. Soliman N.F. Algarni A.D. Abd el-Samie F.E. Computers and Systems Department Electronics Research Institute El Nozha Huckstep Cairo 12622 Egypt Department of electronics and electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf 32952 Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh 11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh 84428 Saudi Arabia

Lung cancer remains a major concern in modern oncology due to its high mortality rates and multifaceted origins, including hereditary factors and various clinical changes. It stands as the deadliest type of cancer and a significant cause of cancer-related deaths globally. Early diagnosis enables healthcare providers to administer appropriate treatment measures promptly and accurately, leading to improved prognosis and higher survival rates. The significant increase in both the incidence and mortality rates of lung cancer, particularly its ranking as the second most prevalent cancer among w.men w.rldw.de, underscores the need for comprehensive research into efficient screening methods. Advances in diagnostic techniques, particularly the use of computed tomography (CT) scans, have revolutionized the identification of lung cancer. CT scans are renow.ed for their ability to provide high-resolution images and are particularly effective in detecting small, calcified areas, crucial for identifying early-stage lung cancer. Consequently, there is grow.ng interest in enhancing computer-aided detection (CAD) systems. These algorithms assist radiologists by reducing false-positive interpretations and improving the accuracy of early cancer diagnosis. This study aims to enhance the effectiveness of CAD systems through various methods. Initially, the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm is employed to preprocess CT scan images, thereby improving their visual quality. Further refinement is achieved by integrating different optimization strategies w.th the CLAHE method. The CutMix data augmentation technique is applied to boost the performance of the proposed model. A comparative analysis is conducted using deep learning architectures such as Inception-V3, ResNet101, Xception, and EfficientNet. The study evaluates the performance of these architectures in image classification tasks, both w.th and w.thout the implementation of the CLAHE algorithm. The em

关键词： CLAHE algorithm classification deep learning feature extraction healthcare applications Lung cancer detection optimization

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A Hybrid Cybersecurity Algorithm for Digital Image Transmission over Advanced Communication Channel Models

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Computers, Materials & Continua 2024年第4期79卷 201-241页

作者： Naglaa F.Soliman Fatma E.Fadl-Allah w.lid el-shafai Mahmoud I.Aly Maali Alabdulhafith Fathi E.Abd el-Samie Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of electronics and Communications Faculty of EngineeringZagazig UniversityZagazig44519Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of electronics and electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt

The efficient transmission of images,w.ich plays a large role inw.reless communication systems,poses a significant challenge in the grow.h of multimedia ***-quality images require w.0}l-tuned communication *** Single Carrier Frequency Division Multiple Access(SC-FDMA)is adopted for broadband w.reless communications,because of its low.sensitivity to carrier frequency offsets and low.Peak-to-Average Pow.r Ratio(PAPR).Data transmission through open-channel netw.rks requires much concentration on security,reliability,and *** data need a space aw.y fromunauthorized access,modification,or *** requirements are to be fulfilled by digital image w.termarking and *** paper ismainly concerned w.th secure image communication over the w.reless SC-FDMA systemas an adopted communication *** introduces a robust image communication framew.rk over SC-FDMA that comprises digital image w.termarking and encryption to improve image security,w.ile maintaining a high-quality reconstruction of images at the receiver *** proposed framew.rk allow. image w.termarking based on the Discrete Cosine Transform(DCT)merged w.th the Singular Value Decomposition(SVD)in the so-called DCT-SVD *** addition,image encryption is implemented based on chaos and DNA *** encrypted w.termarked images are then transmitted through the w.reless SC-FDMA *** linearMinimumMean Square Error(MMSE)equalizer is investigated in this paper to mitigate the effect of channel fading and noise on the transmitted *** subcarrier mapping schemes,namely localized and interleaved schemes,are compared in this *** study depends on different channelmodels,namely PedestrianAandVehicularA,w.th a modulation technique namedQuadratureAmplitude Modulation(QAM).Extensive simulation experiments are conducted and introduced in this paper for efficient transmission of encrypted w.termarked *** addition,different variants of SC-FDMA bas

关键词： Cybersecurity applications image transmission channel models modulation techniques w.termarking and encryption

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A Fractional Fourier Based Medical Image Authentication Approach

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Computers, Materials & Continua 2022年第2期70卷 3133-3150页

作者： Fayez Alqahtani Mohammed Amoon w.lid el-shafai Department of Softw.re Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11451Saudi Arabia Department of Computer Science Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Department of Computer Science and Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of electronics and electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt

Patient medical information in all forms is crucial to keep private and secure,particularly w.en medical data communication occurs through insecure ***,there is a bad need for protecting and securing the color medical images against impostors and *** this paper,an optical medical image security approach is *** is based on the optical bit-plane Jigsaw.Transform(JT)and Fractional Fourier Transform(FFT).Different kernels w.th a lone lens and a single arbitrary phase code are exploited in this security approach.A preceding bit-plane scrambling process is conducted on the input color medical images prior to the JT and FFT processes to accomplish a tremendous level of robustness and *** confirm the efficiency of the suggested security approach for secure color medical image communication,various assessments on different color medical images are examined based on different statistical security ***,a comparative analysis is introduced betw.en the suggested security approach and other conventional cryptography *** simulation outcomes acquired for performance assessment demonstrate that the suggested security approach is highly *** has excellent encryption/decryption performance and superior security results compared to conventional cryptography approaches w.th achieving recommended values of average entropy and correlation coefficient of 7.63 and 0.0103 for encrypted images.

关键词： Medical image security encryption JT FFT cryptosystem

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Novel Ransomw.re Hiding Model Using HEVC Steganography Approach

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Computers, Materials & Continua 2022年第1期70卷 1209-1228页

作者： Iman Almomani Aala AlKhayer w.lid el-shafai Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Computer Science Department King Abdullah II School for Information TechnologyThe University of Jordan11942Jordan Department of electronics and electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt

Ransomw.re is considered one of the most threatening *** solutions have focused mainly on discriminating ransomw.re by analyzing the apps themselves,but they have overlooked possible w.ys of hiding ransomw.re apps and making them difficult to be detected and then ***,this paper proposes a novel ransomw.re hiding model by utilizing a block-based High-Efficiency Video Coding(HEVC)steganography *** main idea of the proposed steganography approach is the division of the secret ransomw.re data and cover HEVC frames into different *** that,the Least Significant Bit(LSB)based Hamming Distance(HD)calculation is performed amongst the secret data’s divided blocks and cover ***,the secret data bits are hidden into the marked bits of the cover HEVC frame-blocks based on the calculated HD *** main advantage of the suggested steganography approach is the minor impact on the cover HEVC frames after embedding the ransomw.re w.ile preserving the histogram attributes of the cover video frame w.th a high *** is due to the utilization of an adaptive steganography cost function during the embedding *** proposed ransomw.re hiding approach w.s heavily examined using subjective and objective tests and applying different HEVC streams w.th diverse resolutions and different secret ransomw.re apps of various *** obtained results prove the efficiency of the proposed steganography approach by achieving high capacity and successful embedding process w.ile ensuring the hidden ransomw.re’s undetectability w.thin the video *** example,in terms of embedding quality,the proposed model achieved a high peak signal-to-noise ratio that reached 59.3 dB and a low.mean-square-error of 0.07 for the examined HEVC ***,out of 65 antivirus engines,no engine could detect the existence of the embedded ransomw.re app.

关键词： Ransomw.re embedding steganography HEVC LSB hamming distance applications apk stego security confidentiality

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Digital Tw.n-Based Automated Fault Diagnosis in Industrial IoT Applications

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Computers, Materials & Continua 2023年第4期75卷 183-196页

作者： Samah Alshathri Ezz el-Din Hemdan w.lid el-shafai Amged Sayed Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science and Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Computer Science Department Security Engineering LabPrince Sultan UniversityRiyadh11586Saudi Arabia Department of electronics and electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Industrial electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of electrical Energy Engineering College of Engineering&TechnologyArab Academy for Science Technology&Maritime TransportSmart Village CampusEgypt

In recent years, Digital Tw.n (DT) has gained significant interestfrom academia and industry due to the advanced in information technology,communication systems, Artificial Intelligence (AI), Cloud Computing (CC),and Industrial Internet of Things (IIoT). The main concept of the DT isto provide a comprehensive tangible, and operational explanation of anyelement, asset, or system. How.ver, it is an extremely dynamic taxonomydeveloping in complexity during the life cycle that produces a massive amountof engendered data and information. Likew.se, w.th the development of AI,digital tw.ns can be redefined and could be a crucial approach to aid theInternet of Things (IoT)-based DT applications for transferring the data andvalue onto the Internet w.th better decision-making. Therefore, this paperintroduces an efficient DT-based fault diagnosis model based on machinelearning (ML) tools. In this framew.rk, the DT model of the machine isconstructed by creating the simulation model. In the proposed framew.rk,the Genetic algorithm (GA) is used for the optimization task to improvethe classification accuracy. Furthermore, w. evaluate the proposed faultdiagnosis framew.rk using performance metrics such as precision, accuracy,F-measure, and recall. The proposed framew.rk is comprehensively examinedusing the triplex pump fault diagnosis. The experimental results demonstratedthat the hybrid GA-ML method gives outstanding results compared to MLmethods like LogisticRegression (LR), Na飗e Bayes (NB), and SupportVectorMachine (SVM). The suggested framew.rk achieves the highest accuracyof 95% for the employed hybrid GA-SVM. The proposed framew.rk w.lleffectively help industrial operators make an appropriate decision concerningthe fault analysis for IIoT applications in the context of Industry 4.0.

关键词： Automated fault diagnosis control system ML AI CC IIoT digital tw.ns genetic algorithm GA-ML technique

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An Efficient Intrusion Detection Framew.rk for Industrial Internet of Things Security

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Computer Systems Science & Engineering 2023年第7期46卷 819-834页

作者： Samah Alshathri Ayman el-Sayed w.lid el-shafai Ezz el-Din Hemdan Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science and Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of electronics and electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt

Recently,the Internet of Things(IoT)has been used in various applications such as manufacturing,transportation,agriculture,and healthcare that can enhance efficiency and productivity via an intelligent management console *** the increased use of Industrial IoT(IIoT)applications,the risk of brutal cyber-attacks also *** leads researchers w.rldw.de to w.rk on developing effective Intrusion Detection Systems(IDS)for IoT infrastructure against any malicious ***,this paper provides effective IDS to detect and classify unpredicted and unpredictable severe attacks in contradiction to the IoT infrastructure.A comprehensive evaluation examined on a new.available benchmark TON_IoT dataset is *** data-driven IoT/IIoT dataset incorporates a label feature indicating classes of normal and attack-targeting IoT/IIoT ***,this data involves IoT/IIoT services-based telemetry data that involves operating systems logs and IoT-based traffic netw.rks collected from a realistic medium-scale IoT *** is to classify and recognize the intrusion activity and provide the intrusion detection objectives in IoT environments in an efficient ***,several machine learning algorithms such as Logistic Regression(LR),Linear Discriminant Analysis(LDA),K-Nearest Neighbors(KNN),Gaussian Naive Bayes(NB),Classification and Regression Tree(CART),Random Forest(RF),and AdaBoost(AB)are used for the detection intent on thirteen different intrusion *** performance metrics like accuracy,precision,recall,and F1-score are used to estimate the proposed *** experimental results show.that the CART surpasses the other algorithms w.th the highest accuracy values like 0.97,1.00,0.99,0.99,1.00,1.00,and 1.00 for effectively detecting the intrusion activities on the IoT/IIoT infrastructure on most of the employed *** addition,the proposed w.rk accomplishes high performance compared to other recent rela

关键词： Attacks intrusion detection machine learning deep learning industrial IoT TON_IoT dataset

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An Efficient Intrusion Detection Framew.rk in Softw.re-Defined Netw.rking for Cybersecurity Applications

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Computers, Materials & Continua 2022年第8期72卷 3529-3548页

作者： Ghalib H.Alshammri Amani K.Samha Ezz el-Din Hemdan Mohammed Amoon w.lid el-shafai Department of Computer Science Community CollegeKing Saud UniversityRiyadh28095Saudi Arabia Deanship of Scientific Research Saudi Electronic UniversityRiyadhSaudi Arabia Management Information System Department College of Business AdministrationKing Saud UniversityRiyadh28095Saudi Arabia Department of Computer Science and Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia electronics and electrical Communications Eng.Depart. Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt

Netw.rk management and multimedia data mining techniques have a great interest in analyzing and improving the netw.rk traffic *** recent times,the most complex task in Softw.re Defined Netw.rk(SDN)is security,w.ich is based on a centralized,programmable ***,monitoring netw.rk traffic is significant for identifying and revealing intrusion abnormalities in the SDN ***,this paper provides an extensive analysis and investigation of the NSL-KDD dataset using five different clustering algorithms:K-means,Farthest First,Canopy,Density-based algorithm,and Exception-maximization(EM),using the w.ikato Environment for Know.edge Analysis(w.KA)softw.re to compare extensively betw.en these five ***,this paper presents an SDN-based intrusion detection system using a deep learning(DL)model w.th the KDD(Know.edge Discovery in Databases)***,the utilized dataset is clustered into normal and four major attack categories via the clustering ***,a deep learning method is projected for building an efficient SDN-based intrusion detection *** results provide a comprehensive analysis and a flaw.ess reasonable study of different kinds of attacks incorporated in the KDD ***,the outcomes reveal that the proposed deep learning method provides efficient intrusion detection performance compared to existing *** example,the proposed method achieves a detection accuracy of 94.21%for the examined dataset.

关键词： Deep neural netw.rk DL w.KA netw.rk traffic intrusion and anomaly detection SDN clustering and classification KDD dataset

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Real-Time Multi-Class Infection Classification for Respiratory Diseases

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Computers, Materials & Continua 2022年第11期73卷 4157-4177页

作者： Ahmed el.Shafee w.lid el-shafai Abdulaziz Alarifi Mohammed Amoon Aman Singh Moustafa H.Aly Department of electrical Engineering Faculty of EngineeringAhram Canadian UniversityGizaEgypt Department of electronics and electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Computer Science Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Higher Polytechnic School Universidad Europea del AtlánticoSantander39011Spain electronics and Communications Engineering Department College of Engineering and TechnologyArab Academy for ScienceTechnology and Maritime TransportAlexandria1029Egypt

Real-time disease prediction has emerged as the main focus of study in the field of computerized *** disease identification framew.rk can assist medical practitioners in diagnosing disease in a w.y that is reliable,consistent,and timely,successfully low.ring mortality rates,particularly during endemics and *** prevent this pandemic’s rapid and w.despread,it is vital to quickly identify,confine,and treat affected *** need for auxiliary computer-aided diagnostic(CAD)systems has *** recent studies have indicated that radiological pictures contained critical information regarding the COVID-19 *** advanced convolutional neural netw.rk(CNN)architectures in conjunction w.th radiological imaging makes it possible to provide rapid,accurate,and extremely useful susceptible *** research w.rk proposes a methodology for real-time detection of COVID-19 infections caused by the Corona *** purpose of this study is to offer a tw.-w.y COVID-19(2w.D)diagnosis prediction deep learning system that is built on Transfer Learning Methodologies(TLM)and features customized fine-tuning on top of fully connected layered pre-trained CNN architectures.2w.D has applied modifications to pre-trained models for better *** is designed and implemented to improve the generalization ability of the classifier for binary and multi-class *** w.th the ability to differentiate COVID-19 and No-Patient in the binary class model and COVID-19,No-Patient,and Pneumonia in the multi-class model,our framew.rk is augmented w.th a critical add-on for visually demonstrating infection in any tested radiological image by highlighting the affected region in the patient’s lung in a recognizable color *** proposed system is show. to be extremely robust and reliable for real-time COVID-19 diagnostic *** can also be used to forecast other lung-related *** the system can assist medical practitioner

关键词： COVID-19 real-time computerized disease prediction intelligent disease identification framew.rk CAD systems X-rays CT-scans CNN real-time detection of COVID-19 infections

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Hybrid Single Image Super-Resolution Algorithm for Medical Images

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Computers, Materials & Continua 2022年第9期72卷 4879-4896页

作者： w.lid el-shafai Ehab Mahmoud Mohamed Medien Zeghid Anas MAli Moustafa H.Aly Department of electronics and electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia electrical Engineering Department College of EngineeringPrince Sattam Bin Abdulaziz UniversityWadi Addwasir11991Saudi Arabia electrical Engineering Department Aswan UniversityAswan81542Egypt electronics and Micro-electronics Laboratory(E.μ.E.L) Faculty of SciencesUniversity of MonastirMonastir5000Tunisia Alexandria Higher Institute of Engineering&Technology(AIET) AlexandriaEgypt electronics and Communications Engineering Department College of Engineering and TechnologyArab Academy for ScienceTechnology and Maritime TransportAlexandria1029Egypt

High-quality medical microscopic images used for diseases detection are expensive and difficult to ***,low.resolution images are favorable due to their low.storage space and ease of sharing,w.ere the images can be enlarged w.en needed using Super-Resolution(SR)***,it is important to maintain the shape and size of the medical images w.ile enlarging *** of the problems facing SR is that the performance of medical image diagnosis is very poor due to the deterioration of the reconstructed image ***,this paper suggests a multi-SR and classification framew.rk based on Generative Adversarial Netw.rk(GAN)to generate high-resolution images w.th higher quality and finer details to reduce *** proposed framew.rk comprises five GAN models:Enhanced SR Generative Adversarial Netw.rks(ESRGAN),Enhanced deep SR GAN(EDSRGAN),Sub-Pixel-GAN,SRGAN,and Efficient w.der Activation-B GAN(w.SR-b-GAN).To train the proposed models,w. have employed images from the famous BreakHis dataset and enlarged them by 4×and 16×upscale factors w.th the ground truth of the size of 256×256×***,several evaluation metrics like Peak Signal-to-Noise Ratio(PSNR),Mean Squared Error(MSE),Structural Similarity Index(SSIM),Multiscale Structural Similarity Index(MS-SSIM),and histogram are applied to make comprehensive and objective comparisons to determine the best methods in terms of efficiency,training time,and storage *** obtained results reveal the superiority of the proposed models over traditional and benchmark models in terms of color and texture restoration and detection by achieving an accuracy of 99.7433%.

关键词： GAN medical images SSIM MS-SSIM PSNR SISR

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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 w.lid el-shafai Fathi E.Abd el-Samie Ghada M.el Banby Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department of electronics and Communications Faculty of EngineeringZagazig UniversityZagazig44519Egypt Department of electronics and electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Industrial electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt

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

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

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