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A deep learning fusion model for accurate classification of brain tumours in Magnetic Resonance images

CAAI Transactions on Intelligence Technology 2024年第4期9卷 790-804页

作者： Nechirvan Asaad Zebari Chira Nadheef Moham.ed Dilovan Asaad Zebari Mazin Abed Moham.ed Diyar Qader Zeebaree Haydar Abdulameer Marhoon karrar ham.ed abdulkareem Seifedine Kadry Wattana Viriyasitavat Jan Nedoma Radek Martinek Department of Information Technology Lebanese French UniversityErbilIraq Department of Computer Science University of ZakhoZakhoKurdistan RegionIraq Department of Computer Science College of ScienceNawroz UniversityDuhokKurdistan RegionIraq Department of Artificial Intelligence College of Computer Science and Information TechnologyUniversity of AnbarRamadiIraq Department of Telecommunications VSB‐Technical University of OstravaOstravaCzech Republic Department of Cybernetics and Biomedical Engineering VSB‐Technical University of OstravaOstravaCzech Republic Department of Computer Network and Information Security Technical College of Informatics-AkreDuhok Polytechnic UniversityDuhokIraq Information and Communication Technology Research Group Scientific Research CenterAl‐Ayen UniversityThi‐QarIraq College of Computer Sciences and Information Technology University of KerbalaKarbalaIraq College of Agriculture Al‐Muthanna UniversitySamawahIraq Department of Applied Data Science Noroff University CollegeKristiansandNorway Faculty of Commerce and Accountancy Chulalongkorn Business SchoolChulalongkorn UniversityBangkokThailand

Detecting brain tumours is complex due to the natural variation in their location, shape, and intensity in images. While having accurate detection and segmentation of brain tumours would be beneficial, current methods still need to solve this problem despite the numerous available approaches. Precise analysis of Magnetic Resonance Imaging (MRI) is crucial for detecting, segmenting, and classifying brain tumours in medical diagnostics. Magnetic Resonance Imaging is a vital component in medical diagnosis, and it requires precise, efficient, careful, efficient, and reliable image analysis techniques. The authors developed a Deep Learning (DL) fusion model to classify brain tumours reliably. Deep Learning models require large amounts of training data to achieve good results, so the researchers utilised data augmentation techniques to increase the dataset size for training models. VGG16, ResNet50, and convolutional deep belief networks networks extracted deep features from MRI images. Softmax was used as the classifier, and the training set was supplemented with intentionally created MRI images of brain tumours in addition to the genuine ones. The features of two DL models were combined in the proposed model to generate a fusion model, which significantly increased classification accuracy. An openly accessible dataset from the internet was used to test the model's performance, and the experimental results showed that the proposed fusion model achieved a classification accuracy of 98.98%. Finally, the results were compared with existing methods, and the proposed model outperformed them significantly.

关键词： brain tumour deep learning feature fusion model MRI images multi‐classification

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DeepSVDNet:A Deep Learning-Based Approach for Detecting and Classifying Vision-Threatening Diabetic Retinopathy in Retinal Fundus Images

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Computer Systems Science & Engineering 2024年第2期48卷 511-528页

作者： Anas Bilal Azhar Imran Talha Imtiaz Baig Xiaowen Liu Haixia Long abdulkareem Alzahrani Muhammad Shafiq College of Information Science and Technology Hainan Normal UniversityHaikou571158China Department of Creative Technologies Air UniversityIslamabadPakistan School of Life Science and Technology University of Electronic Science and Technology of China UESTCChengduChina School of Science and Technology University of Management and Technology UMTLahorePunjabPakistan Faculty of Computer Science&Information Technology Al Baha UniversityAl BahaSaudi Arabia School of Information Engineering Qujing Normal UniversityQujingChina

Artificial Intelligence(AI)is being increasingly used for diagnosing Vision-Threatening Diabetic Retinopathy(VTDR),which is a leading cause of visual impairment and blindness ***,previous automated VTDR detection methods have mainly relied on manual feature extraction and classification,leading to *** paper proposes a novel VTDR detection and classification model that combines different models through majority *** proposed methodology involves preprocessing,data augmentation,feature extraction,and classification *** use a hybrid convolutional neural network-singular value decomposition(CNN-SVD)model for feature extraction and selection and an improved SVM-RBF with a Decision Tree(DT)and K-Nearest Neighbor(KNN)for *** tested our model on the IDRiD dataset and achieved an accuracy of 98.06%,a sensitivity of 83.67%,and a specificity of 100%for DR detection and evaluation tests,*** proposed approach outperforms baseline techniques and provides a more robust and accurate method for VTDR detection.

关键词： Diabetic retinopathy(DR) fundus images(FIs) support vector machine(SVM) medical image analysis convolutional neural networks(CNN) singular value decomposition(SVD) classification

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Acute Transverse Myelitis in Pregnancy—A Case Report and a Literature Review

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Open Journal of Obstetrics and Gynecology 2024年第9期14卷 1512-1521页

作者： Huda Abbas Ali Isa Fatema Hasan Zainab Mohd Al Aswad Naeema Ahmed abdulkareem Salmaniya Medical Complex Manama Bahrain Ob Gyn Faculty Arabian Gulf University Manama Bahrain

Purpose: This case report highlights a rare instance of a 26-week pregnant woman presenting with limb numbness, diagnosed with acute transverse myelitis (ATM) following COVID-19 vaccination. Notably, she tested positive for neuromyelitis optica (NMO) but did not exhibit the typical symptoms of blurred vision. Methods: Data were collected from I Seha in governmental hospitals in Bahrain, providing a comprehensive overview of the patient’s clinical journey. Results: The patient was treated with carbamazepine, steroids, and clexane, leading to the successful delivery of a healthy baby at 34 weeks. However, two years postpartum, she reported new episodes of blurred vision and numbness. Subsequent treatment with Rituximab resulted in significant improvement, with a diagnosis of Devic’s disease established. Conclusion: Acute transverse myelitis is a rare condition, particularly during pregnancy, and in this case, symptoms of Devic’s disease were masked by the absence of blurred vision. This condition predominantly affects females in their 20s during childbearing years and can lead to complications such as premature delivery. Effective management includes steroids and antiepileptic drugs, with clexane playing a crucial role in preventing embolisms. A delivery mode may vary based on obstetric factors, and MRI remains the gold standard for diagnosis. Close follow-up is essential for optimal patient outcomes.

关键词： Acute Transverse Myelitis (ATM) COVID-19 Devic’s Syndrome Neuromyelitis Optica (NMO) Obstetric Management of ATM

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Statistical Medical Pattern Recognition for Body Composition Data Using Bioelectrical Impedance Analyzer

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Computers, Materials & Continua 2021年第5期67卷 2601-2617页

作者： Florin Valentin Leuciuc Maria Daniela Craciun Iulian Stefan Holubiac Mazin Abed Moham.ed karrar ham.ed abdulkareem Gheorghe Pricop Stefan cel Mare University of Suceava Suceava720229Romania The Interdisciplinary Research Center for Human Motricity and Health Sciences Suceava720229Romania College of Computer Science and Information Technology University of AnbarRamadi31001Iraq College of Agriculture Al-Muthanna UniversitySamawah66001Iraq

Identifying patterns,recognition systems,prediction methods,and detection methods is a major challenge in solving different medical *** categories of devices for personal and professional assessment of body composition are *** impedance analyzer is a simple,safe,affordable,mobile,non-invasive,and less expensive alternative device for body composition *** the body composition pattern of different groups with varying age and gender is a major challenge in defining an optimal level because of the body shape,body mass,energy requirements,physical fitness,health status,and metabolic ***,this research aims to identify the statistical medical pattern recognition of body composition data by using a bioelectrical impedance *** previous studies,a pattern was identified for four indicators that concern body composition(e.g.,body mass index(BMI),body fat,muscle mass,and total body water).The novelty of our study is the fact that we identified a recognition pattern by using medical statistical methods for a body composition that contains seven indicators(e.g.,body fat,visceral fat,BMI,muscle mass,skeletal muscle mass,sarcopenic index,and total body water).The youth that exhibited the body composition pattern identified in our study could be considered *** deviation of one or more parameters outside the margins of the pattern for body composition could be associated with health issues,and more medical investigations would be needed for a *** is considered a valid and reliable device to assess body composition along with medical statistical methods to identify a pattern for body composition according to the age,gender,and other relevant parameters.

关键词： Statistical method pattern recognition body composition assessment

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Parametric Methods for the Regional Assessment of Cardiac Wall Motion Abnormalities: Comparison Study

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Computers, Materials & Continua 2021年第10期69卷 1233-1252页

作者： Narjes Benameur Mazin Abed Moham.ed Ramzi Mahmoudi Younes Arous Begonya Garcia-Zapirain karrar ham.ed abdulkareem Mohamed Hedi Bedoui University of Tunis El Manar Higher Institute of Medical Technologies of TunisLaboratory of Biophysics and Medical TechnologiesTunisTunisia College of Computer Science and Information Technology University of Anbar31001AnbarIraq Faculty of Medicine of Monastir Medical Imaging Technology Lab-LTIM-LR12ES06University of Monastir5019MonastirTunisia Gaspard-Monge Computer-Science Laboratory Paris-Est UniversityMixed Unit CNRS-UMLV-ESIEE UMR8049BP99ESIEE Paris CitéDescartes93162 Noisy Le GrandFrance Military Hospital of Instruction of Tunis TunisTunisia eVIDA Lab University of DeustoAvda/Universidades 24Bilbao48007Spain College of Agriculture Al-Muthanna UniversitySamawah66001Iraq

Left ventricular(LV)dysfunction is mainly assessed by global contractile indices such as ejection fraction and LV Volumes in cardiac *** these indices give information about the presence or not of LV alteration,they are not able to identify the location and the size of such *** aim of this study is to compare the performance of three parametric imaging techniques used in cardiac MRI for the regional quantification of cardiac *** proposed approaches were evaluated on 20 patients with myocardial infarction and 20 subjects with normal *** parametric images approaches:covariance analysis,parametric images based on Hilbert transform and those based on the monogenic signal were evaluated using cine-MRI frames acquired in three planes of *** results show that parametric images generated from the monogenic signal were superior in term of sensitivity(89.69%),specificity(86.51%)and accuracy(89.06%)to those based on covariance analysis and Hilbert transform in the detection of contractile dysfunction related to myocardial ***,the parametric image based on the monogenic signal is likely to provide additional regional indices about LV dysfunction and it may be used in clinical practice as a tool for the analysis of the myocardial alterations.

关键词： Covariance analysis cardiac MRI monogenic signal assessment Hilbert transform

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A New Multi-Agent Feature Wrapper Machine Learning Approach for Heart Disease Diagnosis

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Computers, Materials & Continua 2021年第4期67卷 51-71页

作者： Mohamed Elhoseny Mazin Abed Moham.ed Salama A.Mostafa karrar ham.ed abdulkareem Mashael S.Maashi Begonya Garcia-Zapirain Ammar Awad Mutlag Marwah Suliman Maashi Department of Computer Science College of Computer Information TechnologyAmerican University in the Emirates503000United Arab Emirates College of Computer Science and Information Technology University of AnbarRamadi31001Iraq Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn MalaysiaJohor86400Malaysia College of Agriculture Al-Muthanna UniversitySamawah66001Iraq Department of Software Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11451Saudi Arabia eVIDA Lab University of DeustoBilbao48007Spain Department of Pure Science Ministry of EducationGeneral Directorate of CurriculaBaghdadIraq Department of Medical Laboratory Science Faculty of Applied Medical ScienceKing Abdulaziz UniversityJeddah21589Saudi Arabia

Heart disease(HD)is a serious widespread life-threatening *** heart of patients with HD fails to pump sufcient amounts of blood to the entire *** the occurrence of HD early and efciently may prevent the manifestation of the debilitating effects of this disease and aid in its effective *** methods for diagnosing HD are sometimes unreliable and insufcient in analyzing the related *** an alternative,noninvasive medical procedures based on machine learning(ML)methods provide reliable HD diagnosis and efcient prediction of HD ***,the existing models of automated ML-based HD diagnostic methods cannot satisfy clinical evaluation criteria because of their inability to recognize anomalies in extracted symptoms represented as classication features from patients with *** this study,we propose an automated heart disease diagnosis(AHDD)system that integrates a binary convolutional neural network(CNN)with a new multi-agent feature wrapper(MAFW)*** MAFW model consists of four software agents that operate a genetic algorithm(GA),a support vector machine(SVM),and Naïve Bayes(NB).The agents instruct the GA to perform a global search on HD features and adjust the weights of SVM and BN during initial classication.A nal tuning to CNN is then performed to ensure that the best set of features are included in HD *** CNN consists of ve layers that categorize patients as healthy or with HD according to the analysis of optimized HD *** evaluate the classication performance of the proposed AHDD system via 12 common ML techniques and conventional CNN models by using across-validation technique and by assessing six evaluation *** AHDD system achieves the highest accuracy of 90.1%,whereas the other ML and conventional CNN models attain only 72.3%–83.8%accuracy on ***,the AHDD system proposed herein has the highest capability to identify patients with *** system can be used by medical

关键词： Heart disease machine learning multi-agent feature wrapper model heart disease diagnosis HD cleveland datasets convolutional neural network

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COVID-DeepNet: Hybrid Multimodal Deep Learning System for Improving COVID-19 Pneumonia Detection in Chest X-ray Images

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Computers, Materials & Continua 2021年第5期67卷 2409-2429页

作者： A.S.Al-Waisy Mazin Abed Moham.ed Shumoos Al-Fahdawi M.S.Maashi Begonya Garcia-Zapirain karrar ham.ed abdulkareem S.A.Mostafa Nallapaneni Manoj Kumar Dac-Nhuong Le College of Computer Science and Information Technology University of AnbarAnbar31001Iraq College of Computer and Information Sciences King Saud UniversityRiyadh11451Saudi Arabia eVIDA Lab University of Deusto.Avda/UniversidadesBilbao24.48007Spain College of Agriculture Al-Muthanna UniversitySamawah66001Iraq Faculty of Computer Science and Information Technology University Tun Hussein Onn MalaysiaJohor86400Malaysia School of Energy and Environment City University of Hong KongKowloon83Hong Kong Institute of Research and Development Duy Tan UniversityDanang550000Vietnam Faculty of Information Technology Duy Tan UniversityDanang550000Vietnam

Coronavirus(COVID-19)epidemic outbreak has devastating effects on daily lives and healthcare systems *** newly recognized virus is highly transmissible,and no clinically approved vaccine or antiviral medicine is currently *** diagnosis of infected patients through effective screening is needed to control the rapid spread of this *** radiography imaging is an effective diagnosis tool for COVID-19 virus and ***,a novel hybrid multimodal deep learning system for identifying COVID-19 virus in chest X-ray(CX-R)images is developed and termed as the COVID-DeepNet system to aid expert radiologists in rapid and accurate image ***,Contrast-Limited Adaptive Histogram Equalization(CLAHE)and Butterworth bandpass filter were applied to enhance the contrast and eliminate the noise in CX-R images,*** from two different deep learning approaches based on the incorporation of a deep belief network and a convolutional deep belief network trained from scratch using a large-scale dataset were then *** architecture,which provides radiologists a high degree of confidence to distinguish healthy and COVID-19 infected people,was *** proposed COVID-DeepNet system can correctly and accurately diagnose patients with COVID-19 with a detection accuracy rate of 99.93%,sensitivity of 99.90%,specificity of 100%,precision of 100%,F1-score of 99.93%,MSE of 0.021%,and RMSE of 0.016%in a large-scale *** system shows efficiency and accuracy and can be used in a real clinical center for the early diagnosis of COVID-19 virus and treatment follow-up with less than 3 s per image to make the final decision.

关键词： Coronavirus epidemic deep learning deep belief network convolutional deep belief network chest radiography imaging

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An Optimal Distribution of RSU for Improving Self-Driving Vehicle Connectivity

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Computers, Materials & Continua 2022年第2期70卷 3311-3319页

作者： Khattab Alheeti abdulkareem Alaloosy Haitham Khalaf abdulkareem Alzahrani Duaa Al_Dosary Department of Computer Networking System College of Computer Sciences and Information TechnologyUniversity of AnbarRamadiIraq College of Medicine University of AnbarRamadiIraq Department of Computer Science Faculty of Computer Science and Information TechnologyAlbaha UniversityAlbahaSaudi Arabia College of Computer Sciences and Information Technology University of AnbarRamadiIraq

Self-driving and semi-self-driving cars play an important role in our daily *** effectiveness of these cars is based heavily on the use of their surrounding areas to collect sensitive and vital ***,external infrastructures also play significant roles in the transmission and reception of control data,cooperative awareness messages,and caution *** this case,roadside units are considered one of themost important communication *** distribution of these infrastructures will overburden the spread of self-driving vehicles in terms of cost,bandwidth,connectivity,and radio coverage *** this paper,a new distributed roadside unit is proposed to enhance the performance and connectivity of these ***,this approach is based primarily on k-means to find the optimal location of each roadside *** addition,this approach supports dynamicmobility with a long period of connectivity for each ***,this system can adapt to various locations(e.g.,highways,rural areas,urban environments).The simulation results of the proposed system are reflected in its efficiency and ***,the system can achieve a high connectivity rate with a low error rate while reducing costs.

关键词： Self-driving cars roadside unit autonomous vehicles distributed systems connectivity

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A Comprehensive Investigation of Machine Learning Feature Extraction and ClassificationMethods for Automated Diagnosis of COVID-19 Based on X-ray Images

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Computers, Materials & Continua 2021年第3期66卷 3289-3310页

作者： Mazin Abed Moham.ed karrar ham.ed abdulkareem Begonya Garcia-Zapirain Salama A.Mostafa Mashael S.Maashi Alaa S.Al-Waisy Moham.ed Ahmed Subhi Ammar Awad Mutlag Dac-Nhuong Le College of Computer Science and Information Technology University of AnbarRamadi31001Iraq College of Agriculture Al-Muthanna UniversitySamawah66001Iraq eVIDA Lab University of DeustoBilbao48007Spain Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn MalaysiaJohor86400Malaysia Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11451Saudi Arabia Faculty of Engineering and Built Environment Universiti Kebangsaan MalaysiaBangi43600Malaysia Pure Science Department Ministry of EducationGeneral Directorate of CurriculaBaghdad10Iraq Institute of Research and Development Duy Tan UniversityDa Nang550000Vietnam Faculty of Information Technology Duy Tan UniversityDa Nang550000Vietnam

The quick spread of the CoronavirusDisease(COVID-19)infection around the world considered a real danger for global *** biological structure and symptoms of COVID-19 are similar to other viral chest maladies,which makes it challenging and a big issue to improve approaches for efficient identification of COVID-19 *** this study,an automatic prediction of COVID-19 identification is proposed to automatically discriminate between healthy and COVID-19 infected subjects in X-ray images using two successful moderns are traditional machine learning methods(e.g.,artificial neural network(ANN),support vector machine(SVM),linear kernel and radial basis function(RBF),k-nearest neighbor(k-NN),Decision Tree(DT),andCN2 rule inducer techniques)and deep learningmodels(e.g.,MobileNets V2,ResNet50,GoogleNet,DarkNet andXception).A largeX-ray dataset has been created and developed,namely the COVID-19 ***(400 healthy cases,and 400 COVID cases).To the best of our knowledge,it is currently the largest publicly accessible COVID-19 dataset with the largest number of X-ray images of confirmed COVID-19 infection *** on the results obtained from the experiments,it can be concluded that all the models performed well,deep learning models had achieved the optimum accuracy of 98.8%in ResNet50 *** comparison,in traditional machine learning techniques, the SVM demonstrated the best result for an accuracy of 95% and RBFaccuracy 94% for the prediction of coronavirus disease 2019.

关键词： Coronavirus disease COVID-19 diagnosis machine learning convolutional neural networks resnet50 artificial neural network support vector machine X-ray images feature transfer learning

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Modeling the rate of corrosion of carbon steel using activated diethanolamine solutions for CO2 absorption

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Chinese Journal of Chemical Engineering 2020年第8期28卷 2099-2110页

作者： Lubna Ghalib Ahmed abdulkareem Brahim Si Ali Shaukat Ali Mazari Materials Engineering Department Mustansiriyah UniversityBaghdad 14022Iraq Mathematics Science Directorate General of Education Rusafa 1BaghdadIraq Department of Chemical Engineering University of Malaya50603 Kuala LumpurMalaysia Department of Chemical Engineering Dawood University of Engineering and TechnologyKarachi 74800Pakistan

A mechanistic model is developed to investigate the influence of an activator on the corrosion rate of carbon steel in the absorption processes of carbon dioxide(CO2).Piperazine(PZ)is used as the activator in diethanolamine(DEA)aqueous *** developed model for corrosion takes into consideration the effect of fluid flow,transfer of charge and diffusion of oxidizing agents and operating parameters like temperature,activator concentration,CO2 loading and *** study consists of two major models:Vapor–liquid Equilibrium(VLE)model and electrochemical corrosion *** electrolyte-NRTL equilibrium model was used for determination of concentration of chemical species in the bulk *** results of speciation were subsequently used for producing polarization curves and predicting the rate of corrosion occurring at the surface of *** increase in concentration of activator,increases the rate of corrosion of carbon steel in mixtures of activated DEA.

关键词： CO2 capture CO2 corrosion Carbon steel Diethanolamine Piperazine Electrochemical model

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