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Fully Automatic Segmentation of Gynaecological Abnormality Using a New Viola–Jones Model

Computers, Materials & Continua 2021年第3期66卷 3161-3182页

作者： Ihsan Jasim Hussein M.A.Burhanuddin Mazin Abed Mohammed Mohamed Elhoseny Begonya Garcia-Zapirain Marwah Suliman Maashi Mashael S.Maashi BIOCORE Research Group Universiti Teknikal Malaysia MelakaMelaka76100Malaysia Director of UTeM International Centre BIOCORE Research GroupUniversiti Teknikal Malaysia MelakaMelaka76100Malaysia College of Computer Science and Information Technology University of AnbarRamadi31001Iraq Department of Computer Science College of Computer Information TechnologyAmerican University in the Emirates503000United Arab Emirates evida laboratory University of DeustoBilbao48007Spain Medical laboratory Science Department King Abdulaziz UniversityJeddah21589Saudi Arabia Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11451Saudi Arabia

One of the most complex tasks for computer-aided diagnosis(Intelligent decision support system)is the segmentation of ***,this study proposes a new fully automated method for the segmentation of ovarian and breast ultrasound *** main contributions of this research is the development of a novel Viola–James model capable of segmenting the ultrasound images of breast and ovarian cancer *** addition,proposed an approach that can efficiently generate region-of-interest(ROI)and new features that can be used in characterizing lesion *** study uses two databases in training and testing the proposed segmentation *** breast cancer database contains 250 images,while that of the ovarian tumor has 100 images obtained from several hospitals in *** of the experiments showed that the proposed approach demonstrates better performance compared with those of other segmentation methods used for segmenting breast and ovarian ultrasound *** segmentation result of the proposed system compared with the other existing techniques in the breast cancer data set was 78.8%.By contrast,the segmentation result of the proposed system in the ovarian tumor data set was 79.2%.In the classification results,we achieved 95.43%accuracy,92.20%sensitivity,and 97.5%specificity when we used the breast cancer data *** the ovarian tumor data set,we achieved 94.84%accuracy,96.96%sensitivity,and 90.32%specificity.

关键词： Viola-Jones model breast cancer segmentation ovarian tumor ovarian tumor segmentation breast cancer ultrasound images active contour cascade model

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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 Mohammed Ramzi Mahmoudi Younes Arous Begonya Garcia-Zapirain Karrar Hameed 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 Mohammed Salama A.Mostafa Karrar Hameed 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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