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Machine Learning-Based Models for Magnetic Resonance Imaging(MRI)-Based Brain Tumor Classification

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

作者： Abdullah A.Asiri Bilal Khan Fazal Muhammad Shams ur Rahman hassan a.alshamrani Khalaf a.alshamrani Muhammad Irfan Fawaz 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 profession,recent technological advancements play an essential role in the early detection and categorization of many diseases that cause *** technique rising on daily basis for detecting illness in magnetic resonance through pictures is the inspection of ***(computerized)illness detection in medical imaging has found 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 for 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 efﬁ***,in this study,we give an in-depth analysis of six distinct machine learning(ML)algorithms,including Random Forest(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 F1 Score(F1).The training and testing process is strengthened by using a 10-fold cross-validation *** results show that SVM outperforms other algorithms,with 95.3%accuracy.

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

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A Novel Hybrid Machine Learning Approach for Classification 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 Ferzund Tariq Ali Muhammad Aamir Khalaf a.alshamrani hassan a.alshamrani Fawaz F.Alqahtani Muhammad Irfan 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 *** for the diagnosis of brain tumor at initial stages is one of the key step for saving the life of a *** manual segmentation of brain tumor magnetic resonance images(MRIs)takes time and results vary significantly in low-level *** address this issue,we have proposed a ResNet-50 feature extractor depended on multilevel deep convolutional neural network(CNN)for reliable images segmentation by considering the low-level features of *** this model,we have extracted features through ResNet-50 architecture and fed these feature maps to multi-level CNN *** handle the classification 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 for detection and classification of brain *** the model results are calculated in terms of various numerical values identified as precision(P),recall(R),accuracy(Acc)and f1-score(F1-S).The obtained average results are much better as compared to already existing *** modified transfer learning architecture might help the radiologists and doctors as a better significant system for tumor diagnosis.

关键词： Brain tumor magnetic resonance images convolutional neural network classification

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Block-Wise Neural Network for Brain Tumor Identification 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 Irfan Khalaf a.alshamrani hassan a.alshamrani Fawaz 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 for treating tumor *** brain tumor segmentation for cancer analysis from many Magnetic Resonance Images(MRIs)created in medical practice is a problematic and timewasting task for *** a result,there is a critical necessity for more accurate computeraided methods for early tumor *** remove this gap,we enhanced the computational power of a computer-aided system by proposing a finetuned Block-Wise Visual Geometry Group19(BW-VGG19)*** this method,a pre-trained VGG19 is fine-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 from 2005 to 2020 from different 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 outperforms the best results associated with the existing methods.

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

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A U-Net-Based CNN Model for Detection and Segmentation of Brain Tumor

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Computers, Materials & Continua 2023年第1期74卷 1333-1349页

作者： Rehana Ghulam Sammar Fatima Tariq Ali Nazir Ahmad Zafar Abdullah A.Asiri hassan a.alshamrani Samar M.Alqhtani Khlood M.Mehdar Department of Computer Science COMSATS University IslamabadSahiwal CampusSahiwal57000Pakistan Radiological Sciences Department College of applied medical sciencesNajran UniversityNajran61441Saudi Arabia Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Anatomy Department Medicine CollegeNajran UniversityNajran61441Saudi Arabia

Human brain consists of millions of cells to control the overall structure of the human *** these cells start behaving abnormally,then brain tumors *** and initial stage brain tumor detection has always been an issue in the field of medicines for medical *** handle this issue,various deep learning techniques for brain tumor detection and segmentation techniques have been developed,which worked on different datasets to obtain fruitful results,but the problem still exists for the initial stage of detection of brain tumors to save human *** this purpose,we proposed a novel U-Net-based Convolutional Neural Network(CNN)technique to detect and segmentizes the brain tumor for Magnetic Resonance Imaging(MRI).Moreover,a 2-dimensional publicly available Multimodal Brain Tumor Image Segmentation(BRATS2020)dataset with 1840 MRI images of brain tumors has been used having an image size of 240×240 *** initial dataset preprocessing the proposed model is trained by dividing the dataset into three parts i.e.,testing,training,and validation *** model attained an accuracy value of 0.98%on the BRATS2020 dataset,which is the highest one as compared to the already existing techniques.

关键词： U-net brain tumor magnetic resonance images convolutional neural network segmentation

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A Novel Inherited Modeling Structure of Automatic Brain Tumor Segmentation from 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 Irfan hassan a.alshamrani Fawaz 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 Informatics 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 dreadful worldwide types of cancer and affects people leading to *** resonance imaging methods capture skull images that contain healthy and affected *** checked the affected tissue in the slice-by-slice manner,which was timeconsuming and hectic ***,auto segmentation of the affected part is needed to facilitate ***,we have considered a hybrid model that inherits the convolutional neural network(CNN)properties to the support vector machine(SVM)for 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 for the mathematical measures are precision,accuracy,specificity,sensitivity,and dice ***,our method achieved an accuracy value of 0.98,which is most prominent than existing ***,the proposed approach is more suitable for medical experts to diagnose the early stages of the brain tumor.

关键词： Brain tumor support vector machine convolutional neural network BraTS classification

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LBP–Bilateral Based Feature Fusion for Breast Cancer Diagnosis

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Computers, Materials & Continua 2022年第11期73卷 4103-4121页

作者： Yassir Edrees Almalki Maida Khalid Sharifa Khalid Alduraibi Qudsia Yousaf Maryam Zaffar Shoayea Mohessen Almutiri Muhammad Irfan Mohammad Abd Alkhalik Basha Alaa Khalid Alduraibi Abdulrahman Manaa Alamri Khalaf alshamrani hassan a.alshamrani Division of Radiology Department of MedicineMedical CollegeNajran UniversityNajran 61441Saudi Arabia Department of Computer Science and Information Technology Ibadat International UniversityIslamabad44000Pakistan Department of Radiology College of MedicineQassim UniversityBuraidah 52571Saudi Arabia Department of Radiology King Fahad Specialist HospitalBuraydah 52571Saudi Arabia Electrical Engineering Department College of EngineeringNajran UniversityNajran 61441Saudi Arabia Radiology Department Human Medicine CollegeZagazig UniversityZagazig 44631Egypt Department of Surgery College of MedicineNajran UniversityNajran 61441Saudi Arabia Radiological Sciences Department College of Applied Medical SciencesNajran UniversityNajran 61441Saudi Arabia

Since reporting cases of breast cancer are on the rise all over the *** in regions such as Pakistan,Saudi Arabia,and the United *** methods for the early detection and diagnosis of breast cancer are *** usual diagnosis procedures followed by physicians has been updated with modern diagnostic approaches that include computer-aided support for better *** learning based practices has increased the accuracy and efficiency of medical diagnosis,which has helped save lives of many *** is much research in the field of medical imaging diagnostics that can be applied to the variety of data such as magnetic resonance images(MRIs),mammograms,X-rays,ultrasounds,and histopathological images,but magnetic resonance(MR)and mammogram imaging have proved to present the promising *** proposed paper has presented the results of classification algorithms over Breast Cancer(BC)mammograms from a novel dataset taken from hospitals in the Qassim health cluster of Saudi *** paper has developed a novel approach called the novel spectral extraction algorithm(NSEA)that uses feature extraction and fusion by using local binary pattern(LBP)and bilateral algorithms,as well as a support vector machine(SVM)as a *** NSEA with the SVM classifier demonstrated a promising accuracy of 94%and an elapsed time of 0.68 milliseconds,which were significantly better results than those of comparative experiments from classifiers named Naïve Bayes,logistic regression,K-Nearest Neighbor(KNN),Gaussian Discriminant Analysis(GDA),AdaBoost and Extreme Learning Machine(ELM).ELM produced the comparative accuracy of 94%however has a lower elapsed time of 1.35 as compared to *** has produced a fairly well accuracy of 82%,KNN has a low accuracy of 66%.However Logistic Regression,GDA and Naïve Bayes have produced the lowest accuracies of 47%,43%and 42%.

关键词： Artificial intelligence machine learning breast cancer mammograms supervised learning classification feature fusion

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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 Sharifa Khalid Alduraibi Shoayea Mohessen Almutiri Muhammad Irfan 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 for 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 for mammographic images is always an interesting task,even for *** this work,to detect and classify 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 from different hospitals in the Qassim health cluster of Saudi *** collection of all categories of BI-RADs is one of the major contributions of this *** significant 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 classification accuracy,sensitivity,specificity,precision,and F1-score of 93.6%,94.8%,96.9%,96.6%,and 95.7%,*** proposed model achieved better performance compared to previously available methods.

关键词： Breast cancer CNN SVM BIRADS classification

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Enhanced Adaptive Brain-Computer Interface Approach for Intelligent Assistance to Disabled Peoples

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Computer Systems Science & Engineering 2023年第8期46卷 1355-1369页

作者： Ali Usman Javed Ferzund Ahmad Shaf Muhammad Aamir Samar Alqhtani Khlood M.Mehdar Hanan Talal Halawani hassan a.alshamrani Abdullah A.Asiri Muhammad Irfan Department of Computer Science COMSATS University IslamabadSahiwal CampusSahiwal57000Pakistan Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Anatomy Department Medicine CollegeNajran UniversityNajran61441Saudi Arabia Computer Science Department College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Radiological Sciences Department College of Applied Medical Sciences and Information SystemsNajran UniversityNajran61441Kingdom of Saudi Arabia Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia

Assistive devices for disabled people with the help of Brain-Computer Interaction(BCI)technology are becoming vital bio-medical *** with physical disabilities need some assistive devices to perform their daily *** these devices,higher latency factors need to be addressed ***,the main goal of this research is to implement a real-time BCI architecture with minimum latency for command *** proposed architecture is capable to communicate between different modules of the system by adopting an automotive,intelligent data processing and classification ***-sky mind wave device has been used to transfer the data to our implemented server for command ***-Net Convolutional Neural Network(TN-CNN)architecture has been proposed to recognize the brain signals and classify them into six primary mental states for data *** collection and processing are the responsibility of the central integrated server for system load *** of implemented architecture and deep learning model shows excellent *** proposed system integrity level was the minimum data loss and the accurate commands processing *** training and testing results are 99%and 93%for custom model implementation based on *** proposed real-time architecture is capable of intelligent data processing unit with fewer errors,and it will benefit assistive devices working on the local server and cloud server.

关键词： Disable person electroencephalogram convolutional neural network brain signal classification

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Multi-Level Deep Generative Adversarial Networks for Brain Tumor Classification on Magnetic Resonance Images

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Intelligent Automation & Soft Computing 2023年第4期36卷 127-143页

作者： Abdullah A.Asiri Ahmad Shaf Tariq Ali Muhammad Aamir Ali Usman Muhammad Irfan hassan a.alshamrani Khlood M.Mehdar Osama M.Alshehri Samar M.Alqhtani Radiological Sciences Department College of Applied Medical SciencesNajran UniversityNajran61441Saudi Arabia Department of Computer Science COMSATS University IslamabadSahiwal CampusSahiwal57000Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Anatomy Department Medicine CollegeNajran UniversityNajranSaudi Arabia Department of Clinical Laboratory Sciences Collage of Applied Medical ScienceNajran UniversityNajranSaudi Arabia Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia

The brain tumor is an abnormal and hysterical growth of brain tissues,and the leading cause of death affected patients *** in this technol-ogy-based arena,brain tumor images with proper labeling and acquisition still have a problem with the accurate and reliable generation of realistic images of brain tumors that are completely different from the original *** artiﬁcially created medical image data would help improve the learning ability of physicians and other computer-aided systems for the generation of augmented *** over-come the highlighted issue,a Generative Adversarial Network(GAN)deep learn-ing technique in which two neural networks compete to become more accurate in creating artiﬁcially realistic data for MRI *** GAN network contains mainly two parts known as generator and ***,a generator is the convolutional neural network,and a discriminator is the deconvolutional neural *** this research,the publicly accessible Contrast-Enhanced Mag-netic Resonance Imaging(CE-MRI)dataset collected from 2005-to 2020 from different hospitals in China consists of four classes has been *** proposed method is simple and achieved an accuracy of 96%.We compare our technique results with the existing results,indicating that our proposed technique outper-forms the best results associated with the existing methods.

关键词： GAN network CE-MRI images convolutional neural network brain tumor classiﬁcation

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Human Emotions Classification Using EEG via Audiovisual Stimuli and AI

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Computers, Materials & Continua 2022年第12期73卷 5075-5089页

作者： Abdullah A Asiri Akhtar Badshah Fazal Muhammad hassan a.alshamrani Khalil Ullah Khalaf a.alshamrani Samar Alqhtani Muhammad Irfan Hanan Talal Halawani Khlood M Mehdar Radiological Sciences Department College of Applied Medical SciencesNajran UniversityNajranSaudi Arabia Faculty of Computer Science and Engineering GIK Institute of Engineering Sciences and TechnologyTopi23460Pakistan Department of Electrical Engineering University of Engineering and TechnologyMardan23200Pakistan Department of Software Engineering University of MalakandDir LowerPakistan Department of Computer Science and Information Systems College of EngineeringNajran University Saudi ArabiaNajran61441Saudi Arabia Electrical Engineering Department College of EngineeringNajran University Saudi ArabiaNajran61441Saudi Arabia Computer Science Department College of Computer Science and Information SystemsNajran UniversityNajranSaudi Arabia Anatomy Department Medicine CollegeNajran UniversityNajranSaudi Arabia

Electroencephalogram(EEG)is a medical imaging technology that can measure the electrical activity of the scalp produced by the brain,measured and recorded chronologically the surface of the scalp from the *** recorded signals from the brain are rich with useful *** inference of this useful information is a challenging *** paper aims to process the EEG signals for the recognition of human emotions specifically happiness,anger,fear,sadness,and surprise in response to audiovisual *** EEG signals are recorded by placing neurosky mindwave headset on the subject’s scalp,in response to audiovisual stimuli for the mentioned *** a bandpass filter with a bandwidth of 1-100 Hz,recorded raw EEG signals are *** preprocessed signals then further analyzed and twelve selected features in different domains are *** Random forest(RF)and multilayer perceptron(MLP)algorithms are then used for the classification of the emotions through extracted *** proposed audiovisual stimuli based EEG emotion classification system shows an average classification accuracy of 80%and 88%usingMLP and RF classifiers respectively on hybrid features for experimental signals of different *** proposed model outperforms in terms of cost and accuracy.

关键词： Electroencephalography emotion classification signal processing multilayer perceptron random forest

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