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Open Journal of.Obstetrics and Gynecology 2016年第12期6卷 761-768页

作者： Lujain Bukhari Ayman A. Bukhari Omar f. Albakri arwa f. alshamrani Walaa E. Alahmadi Hassan S. O. Abduljabbar Obstetrics and Gynecology Department King Abdulaziz University Hospital Jeddah Saudi Arabia Biochemistry Department King Abdulaziz University Hospital Jeddah Saudi Arabia Department of.Obstetrics & Gynecology Medical College King Abdulaziz University Jeddah Saudi Arabia

The aims are to review respectively 229 cases of.hysterectomy and to f.nd the f.ctor af.ecting the decision of.vaginal hysterectomy. Data collected f.om medical charts include age, nationality, parity, menopause, history of.Dilation and curettage. The f.llowing data were obtained: the clinical presentation and the indication f.r hysterectomy. Postoperative complication includes the estimated blood loss, the number of.days in hospital and ICU admission recorded. The route of.hysterectomy f.und that 187 (82%) were abdominal and only 42 (18%) were vaginal. f.ctors signif.cantly associated with the choice of.vaginal approach: age, parity, smaller uterine size and prolapse. f.ctors af.ecting the decision of.vaginal hysterectomy f.r treatment of.benign diseases are identif.ed as f.llows: if.the age is more than 35 years or if.the women already reach menopause, the presenting symptoms are not vaginal bleeding but prolapsed uterus and uterine size is less than 12 weeks.

关键词： Vaginal Abdominal Hysterectomy

Machine Learning-Based Models f.r Magnetic Resonance Imaging(MRI)-Based Brain Tumor Classif.cation

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Intelligent Automation & Sof. Computing 2023年第4期36卷 299-312页

作者： Abdullah A.Asiri Bilal Khan f.zal Muhammad Shams ur Rahman Hassan A.alshamrani Khalaf.A.alshamrani Muhammad Irf.n f.waz f.Alqhtani Department of.Radiological Sciences College of Applied Medical SciencesNajran UniversityNajranSaudi Arabia Department of.Computer Science City University of Science and Information TechnologyPeshawarPakistan Department of.Electrical Engineering University of Engineering and TechnologyMardan23200Pakistan Department of.Computer Science Engineering University of Engineering and TechnologyMardan23200Pakistan Electrical Engineering Department College of EngineeringNajran University Saudi ArabiaNajran61441Saudi Arabia

In the medical prof.ssion,recent technological advancements play an essential role in the early detection and categorization of.many diseases that cause *** technique rising on daily basis f.r detecting illness in magnetic resonance through pictures is the inspection of.***(computerized)illness detection in medical imaging has f.und you the emergent region in several medical diagnostic *** diseases that cause death need to be identiﬁed through such techniques and technologies to overcome the mortality *** brain tumor is one of.the most common causes of.*** have already proposed various models f.r the classiﬁcation and detection of.tumors,each with its strengths and weaknesses,but there is still a need to improve the classiﬁcation process with improved ef.***,in this study,we give an in-depth analysis of.six distinct machine learning(ML)algorithms,including Random f.rest(Rf.,Naïve Bayes(NB),Neural Networks(NN),CN2 Rule Induction(CN2),Support Vector Machine(SVM),and Decision Tree(Tree),to address this gap in improving *** the Kaggle dataset,these strategies are tested using classiﬁcation accuracy,the area under the Receiver Operating Characteristic(ROC)curve,precision,recall,and f. Score(f.).The training and testing process is strengthened by using a 10-f.ld cross-validation *** results show that SVM outperf.rms other algorithms,with 95.3%accuracy.

关键词： MRI images brain tumor machine learning-based classiﬁcation

A Novel Hybrid Machine Learning Approach f.r Classif.cation of.Brain Tumor Images

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Computers, Materials & Continua 2022年第10期73卷 641-655页

作者： Abdullah A.Asiri Amna Iqbal Javed f.rzund Tariq Ali Muhammad Aamir Khalaf.A.alshamrani Hassan A.alshamrani f.waz f.Alqahtani Muhammad Irf.n Ali H.D.Alshehri Radiological Sciences Department College of Applied Medical SciencesNajran UniversityNajran61441Saudi Arabia Department of.Computer Science COMSATS University IslamabadSahiwal CampusSahiwal57000Pakistan College of.Engineering Najran UniversityNajran61441Saudi Arabia

Abnormal growth of.brain tissues is the real cause of.brain *** f.r the diagnosis of.brain tumor at initial stages is one of.the key step f.r saving the lif. of.a *** manual segmentation of.brain tumor magnetic resonance images(MRIs)takes time and results vary signif.cantly in low-level *** address this issue,we have proposed a ResNet-50 f.ature extractor depended on multilevel deep convolutional neural network(CNN)f.r reliable images segmentation by considering the low-level f.atures of.*** this model,we have extracted f.atures through ResNet-50 architecture and f.d these f.ature maps to multi-level CNN *** handle the classif.cation process,we have collected a total number of.2043 MRI patients of.normal,benign,and malignant *** model CNN,multi-level CNN,and ResNet-50 based multi-level CNN have been used f.r detection and classif.cation of.brain *** the model results are calculated in terms of.various numerical values identif.ed as precision(P),recall(R),accuracy(Acc)and f.-score(f.-S).The obtained average results are much better as compared to already existing *** modif.ed transf.r learning architecture might help the radiologists and doctors as a better signif.cant system f.r tumor diagnosis.

关键词： Brain tumor magnetic resonance images convolutional neural network classif.cation

Block-Wise Neural Network f.r Brain Tumor Identif.cation in Magnetic Resonance Images

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Computers, Materials & Continua 2022年第12期73卷 5735-5753页

作者： Abdullah A.Asiri Muhammad Aamir Ahmad Shaf.Tariq Ali Muhammad Zeeshan Muhammad Irf.n Khalaf.A.alshamrani Hassan A.alshamrani f.waz f.Alqahtani Ali H.D.Alshehri Radiological Sciences Department College of Applied Medical SciencesNajran UniversityNajran 61441Saudi Arabia Department of.Computer Science COMSATS University IslamabadSahiwal CampusSahiwal57000Pakistan Department of.Computer Science Bahauddin Zakariya UniversityMultan 66000Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia

The precise brain tumor diagnosis is critical and shows a vital role in the medical support f.r treating tumor *** brain tumor segmentation f.r cancer analysis f.om many Magnetic Resonance Images(MRIs)created in medical practice is a problematic and timewasting task f.r *** a result,there is a critical necessity f.r more accurate computeraided methods f.r early tumor *** remove this gap,we enhanced the computational power of.a computer-aided system by proposing a f.netuned Block-Wise Visual Geometry Group19(BW-VGG19)*** this method,a pre-trained VGG19 is f.ne-tuned with CNN architecture in the block-wise mechanism to enhance the system`s *** publicly accessible Contrast-Enhanced Magnetic Resonance Imaging(CE-MRI)dataset collected f.om 2005 to 2020 f.om dif.erent hospitals in China has been used in this *** proposed method is simple and achieved an accuracy of.0.98%.We compare our technique results with the existing Convolutional Neural network(CNN),VGG16,and VGG19 *** results indicate that our proposed technique outperf.rms the best results associated with the existing methods.

关键词： CNN brain tumor block-wise structure VGG19 VGG16

Liver Ailment Prediction Using Random f.rest Model

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Computers, Materials & Continua 2023年第1期74卷 1049-1067页

作者： f.zal Muhammad Bilal Khan Rashid Naseem Abdullah A Asiri Hassan A alshamrani Khalaf.A alshamrani Samar M Alqhtani Muhammad Irf.n Khlood M Mehdar Hanan Talal Halawani Department of.Electrical Engineering University of Engineering and TechnologyMardan23200Pakistan City University of.Science and Inf.rmation Technology PeshawarPakistan Department of.IT and Computer Science Pak-Austria Fachhochschule Institute of Applied Sciences and TechnologyHaripurPakistan Radiological Sciences Department College of Applied Medical SciencesNajran UniversityNajranSaudi Arabia Department of.Inf.rmation Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Electrical Engineering Department College of EngineeringNajran University Saudi ArabiaNajran61441Saudi Arabia Anatomy Department Medicine CollegeNajran UniversityNajranSaudi Arabia Computer Science Department College of Computer Science and Information SystemsNajran UniversityNajranSaudi Arabia

Today,liver disease,or any deterioration in one’s ability to survive,is extremely common all around the *** research has indicated that liver disease is more f.equent in younger people than in older *** the liver’s capability begins to deteriorate,lif. can be shortened to one or two days,and early prediction of.such diseases is *** several machine learning(ML)approaches,researchers analyzed a variety of.models f.r predicting liver disorders in their early *** a result,this research looks at using the Random f.rest(Rf.classif.er to diagnose the liver disease early *** dataset was picked f.om the University of.Calif.rnia,Irvine ***’s accomplishments are contrasted to those of.Multi-Layer Perceptron(MLP),Average One Dependency Estimator(A1DE),Support Vector Machine(SVM),Credal Decision Tree(CDT),Composite Hypercube on Iterated Random Projection(CHIRP),K-nearest neighbor(KNN),Naïve Bayes(NB),J48-Decision Tree(J48),and f.rest by Penalizing Attributes(f.rest-PA).Some of.the assessment measures used to evaluate each classif.er include Root Relative Squared Error(RRSE),Root Mean Squared Error(RMSE),accuracy,recall,precision,specif.city,Matthew’s Correlation Coef.icient(MCC),f.measure,and *** has an RRSE perf.rmance of.87.6766 and an RMSE perf.rmance of.0.4328,however,its percentage accuracy is *** widely acknowledged result of.this work can be used as a starting point f.r subsequent *** a result,every claim that a new model,f.amework,or method enhances f.recastingmay be benchmarked and demonstrated.

关键词： Liver ailment random f.rest machine learning

A Novel Inherited Modeling Structure of.Automatic Brain Tumor Segmentation f.om MRI

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Computers, Materials & Continua 2022年第11期73卷 3983-4002页

作者： Abdullah AAsiri Tariq Ali Ahmad Shaf.Muhammad Aamir Muhammad Shoaib Muhammad Irf.n Hassan A.alshamrani f.waz f.Alqahtani Osama M.Alshehri Radiological Sciences Department College of Applied Medical SciencesNajran UniversityNajran61441Saudi Arabia Department of.Computer Science COMSATS University IslamabadSahiwal CampusSahiwal57000Pakistan Department of.Inf.rmatics and Systems University of Management and TechnologyLahore54000Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Department of.Clinical Laboratory Sciences Collage of Applied Medical ScienceNajran UniversityNajranSaudi Arabia

Brain tumor is one of.the most dreadf.l worldwide types of.cancer and af.ects people leading to *** resonance imaging methods capture skull images that contain healthy and af.ected *** checked the af.ected tissue in the slice-by-slice manner,which was timeconsuming and hectic ***,auto segmentation of.the af.ected part is needed to f.cilitate ***,we have considered a hybrid model that inherits the convolutional neural network(CNN)properties to the support vector machine(SVM)f.r the auto-segmented brain tumor *** CNN model is initially used to detect brain tumors,while SVM is integrated to segment the tumor region *** proposed method was evaluated on a publicly available BraTS2020 *** statistical parameters used in this work f.r the mathematical measures are precision,accuracy,specif.city,sensitivity,and dice ***,our method achieved an accuracy value of.0.98,which is most prominent than existing ***,the proposed approach is more suitable f.r medical experts to diagnose the early stages of.the brain tumor.

关键词： Brain tumor support vector machine convolutional neural network BraTS classif.cation

Deep Optimal VGG16 Based COVID-19 Diagnosis Model

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Computers, Materials & Continua 2022年第1期70卷 43-58页

作者： M.Buvana K.Muthumayil S.Senthil kumar Jamel Nebhen Sultan S.alshamrani Ihsan Ali Department of.Computer Science and Engineering PSNA College of Engineering and TechnologyDindigul624622India Department of.Inf.rmation Technology PSNA College of Engineering and TechnologyDindigul624622India Department of.Electrical and Electronics Engineering New Prince Shri Bhavani College of Engineering and TechnologyChennai600073India Prince Sattam Bin Abdulaziz University College of Computer Engineering and SciencesAlkharj11942Saudi Arabia Department of.Inf.rmation Technology College of Computer and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of.Computer System and Technology Faculty of Computer Science and Information TechnologyUniversity of MalayaKuala Lumpur50603Malaysia

Coronavirus(COVID-19)outbreak was f.rst identif.ed in Wuhan,China in December *** was tagged as a pandemic soon by the WHO being a serious public medical *** spite of.the f.ct that the virus can be diagnosed by qRT-PCR,COVID-19 patients who are af.ected with pneumonia and other severe complications can only be diagnosed with the help of.Chest X-Ray(CXR)and Computed Tomography(CT)*** this paper,the researchers propose to detect the presence of.COVID-19 through images using Best deep learning model with various *** f.atures like Speeded-Up Robust f.atures(SURf.,f.atures f.om Accelerated Segment Test(f.ST)and Scale-Invariant f.ature Transf.rm(SIf.)are used in the test images to detect the presence of.*** optimal f.atures are extracted f.om the images utilizing DeVGGCovNet(Deep optimal VGG16)model through optimal learning *** task is accomplished by exceptional mating conduct of.Black Widow *** this strategy,cannibalism is *** this phase,f.tness outcomes are rejected and are not satisf.ed by the proposed *** results acquired f.om real case analysis demonstrate the viability of.DeVGGCovNet technique in settling true issues using obscure and testing ***16 model identif.es the imagewhich has a place with which it is dependent on the distinctions in *** impact of.the distinctions on labels during training stage is studied and predicted f.r test *** proposed model was compared with existing state-of.the-art models and the results f.om the proposed model f.r disarray grid estimates like Sen,Spec,Accuracy and f. score were promising.

关键词： COVID 19 multi-f.ature extraction vgg16 optimal learning rate

Breast Cancer Detection in Saudi Arabian Women Using Hybrid Machine Learning on Mammographic Images

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Computers, Materials & Continua 2022年第9期72卷 4833-4851页

作者： Yassir Edrees Almalki Ahmad Shaf.Tariq Ali Muhammad Aamir Sharif. Khalid Alduraibi Shoayea Mohessen Almutiri Muhammad Irf.n Mohammad Abd Alkhalik Basha Alaa Khalid Alduraibi Abdulrahman Manaa Alamri Muhammad Zeeshan Azam Khalaf.alshamrani Hassan A.alshamrani Division of.Radiology Department of MedicineMedical CollegeNajran UniversityNajran61441Kingdom of Saudi Arabia Department of.Computer Science COMSATS University IslamabadSahiwal CampusSahiwal57000Pakistan Department of.Radiology College of MedicineQassim UniversityBuraidah52571Kingdom of Saudi Arabia Department of.Radiology King Fahad Specialist HospitalBuraydah52571Kingdom of Saudi Arabia Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Kingdom of Saudi Arabia Radiology Department Human Medicine CollegeZagazig UniversityZagazig44631Egypt Department of.Surgery College of MedicineNajran UniversityNajran61441Kingdom of Saudi Arabia Department of.Computer Science Bahauddin Zakariya UniversityMultan66000Pakistan Radiological Sciences Department College of Applied Medical SciencesNajran UniversityNajran61441Kingdom of Saudi Arabia

Breast cancer(BC)is the most common cause of.women’s deaths *** mammography technique is the most important modality f.r the detection of.*** detect abnormalities in mammographic images,the Breast Imaging Reporting and Data System(BI-RADs)is used as a *** correct allocation of.BI-RADs categories f.r mammographic images is always an interesting task,even f.r *** this work,to detect and classif. the mammogram images in BI-RADs,a novel hybrid model is presented using a convolutional neural network(CNN)with the integration of.a support vector machine(SVM).The dataset used in this research was collected f.om dif.erent hospitals in the Qassim health cluster of.Saudi *** collection of.all categories of.BI-RADs is one of.the major contributions of.this *** signif.cant contribution is the development of.a hybrid approach through the integration of.CNN and *** proposed hybrid approach uses three CNN models to obtain ensemble CNN model *** ensemble model saves the values to integrate them with *** proposed system achieved a classif.cation accuracy,sensitivity,specif.city,precision,and f.-score of.93.6%,94.8%,96.9%,96.6%,and 95.7%,*** proposed model achieved better perf.rmance compared to previously available methods.

关键词： Breast cancer CNN SVM BIRADS classif.cation

Deep Learning Approach f.r Analysis and Characterization of.COVID-19

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Computers, Materials & Continua 2022年第1期70卷 451-468页

作者： Indrajeet Kumar Sultan S.alshamrani Abhishek Kumar Jyoti Rawat Kamred Udham Singh Mamoon Rashid Ahmed Saeed AlGhamdi School of.Computing Graphic Era Hill UniversityDehradun(UK)India Department of.Inf.rmation Technology College of Computer and Information TechnologyTaif UniversityTaif21944Saudi Arabia School of.Computer Science&IT JAIN(to be Deemed University)BangaloreIndia School of.Computing Dehradun Institute of TechnologyUttarakhandIndia Department of.Computer Engineering Faculty of Science and TechnologyVishwakarma UniversityPuneIndia Department of.Computer Engineering College of Computer and Information TechnologyTaif UniversityTaif21944Saudi Arabia

Early diagnosis of.a pandemic disease like COVID-19 can help deal with a dire situation and help radiologists and other experts manage human resources more *** a recent pandemic,laboratories perf.rm diagnostics manually,which requires a lot of.time and expertise of.the laboratorial technicians to yield accurate ***,the cost of.kits is high,and well-equipped labs are needed to perf.rm this ***,other means of.diagnosis is highly *** is one of.the existing methods that f.nds its use in the diagnosis of.*** radiography observes change in Computed Tomography(CT)chest images of.patients,developing a deep learning-based method to extract graphical f.atures which are used f.r automated diagnosis of.the disease ahead of.laboratory-based *** proposed work suggests an Artif.cial Intelligence(AI)based technique f.r rapid diagnosis of.COVID-19 f.om given volumetric chest CT images of.patients by extracting its visual f.atures and then using these f.atures in the deep learning *** proposed convolutional neural network aims to classif. the inf.ctious and non-inf.ctious SARS-COV2 *** proposed network utilizes 746 chests scanned CT images of.349 images belonging to COVID-19 positive cases,while 397 belong to negative cases of.*** experiment resulted in an accuracy of.98.4%,sensitivity of.98.5%,specif.city of.98.3%,precision of.97.1%,and f.-score of.97.8%.The additional parameters of.classif.cation error,mean absolute error(MAE),root-mean-square error(RMSE),and Matthew’s correlation coef.icient(MCC)are used to evaluate our proposed *** obtained result shows the outstanding perf.rmance f.r the classif.cation of.inf.ctious and non-inf.ctious f.r COVID-19 cases.

关键词： Coronavirus covid-19 respiratory inf.ction computed tomography deep neural network

Heart Disease Risk Prediction Expending of.Classif.cation Algorithms

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Computers, Materials & Continua 2022年第12期73卷 6595-6616页

作者： Nisha Mary Bilal Khan Abdullah A.Asiri f.zal Muhammad Salman Khan Samar Alqhtani Khlood M.Mehdar Hanan Talal Halwani Muhammad Irf.n Khalaf.A.alshamrani City University of.Science and Inf.rmation Technology Peshawar25000Pakistan Radiology Science Department College of.Applied Medical Science Najran University Kingdom of Saudi Arabia Department of.Electrical Engineering University of Engineering and TechnologyMardan23200Pakistan Department of.Inf.rmation Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Anatomy Department Medicine CollegeNajran UniversityNajranSaudi Arabia Electrical Engineering Department College of EngineeringNajran University Saudi ArabiaNajran61441Saudi Arabia

Heart disease prognosis(HDP)is a dif.icult undertaking that requires knowledge and expertise to predict early *** f.ilure is on the rise as a result of.today’s *** healthcare business generates a vast volume of.patient records,which are challenging to manage *** it comes to data mining and machine learning,having a huge volume of.data is crucial f.r getting meaningf.l *** methods f.r predictingHDhave been used by researchers over the last f.w decades,but the f.ndamental concern remains the uncertainty f.ctor in the output data,aswell as the need to decrease the error rate and enhance the accuracy of.HDP assessment ***,in order to discover the optimal HDP solution,this study compares multiple classif.cation algorithms utilizing two separate heart disease datasets f.om the Kaggle repository and the University of.Calif.rnia,Irvine(UCI)machine learning *** a comparative analysis,Mean Absolute Error(MAE),Relative Absolute Error(RAE),precision,recall,f.easure,and accuracy are used to evaluate Linear Regression(LR),Decision Tree(J48),Naive Bayes(NB),Artif.cial Neural Network(ANN),Simple Cart(SC),Bagging,Decision Stump(DS),AdaBoost,Rep Tree(REPT),and Support Vector Machine(SVM).Overall,the SVM classif.er surpasses other classif.ers in terms of.increasing accuracy and decreasing error rate,with RAE of.33.2631 andMAEof.0.165,the precision of.0.841,recall of.0.835,f.measure of.0.833,and accuracy of.83.49 percent f.r the dataset gathered f.om *** SC improves accuracy and reduces the error rate f.r the Kaggle dataset,which is 3.30%f.r RAE,0.016 percent f.r MAE,0.984%f.r precision,0.984 percent f.r recall,0.984 percent f.r f.measure,and 98.44%f.r accuracy.

关键词： Heart disease heart disease datasets evaluation measures classif.cation techniques