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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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CNN-LSTM: A Novel Hybrid Deep Neural Network Model for brain tumor Classification

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Intelligent Automation & Soft Computing 2023年第7期37卷 1129-1143页

作者： R.D.Dhaniya K.M.Umamaheswari Department of Computer Science and Engineering SRM Institute of Science and TechnologyKattankulathur603202India Department of Computing technologies SRM Institute of Science and TechnologyKattankulathur603202India

Current revelations in medical imaging have seen a slew of computer-aided diagnostic(CAD)tools for radiologists *** tumor classification is essential for radiologists to fully support and better interpret magnetic resonance imaging(MRI).In this work,we reported on new observations based on binary brain tumor categorization using HYBRID ***,the collected image is pre-processed and augmented using the following steps such as rotation,cropping,zooming,CLAHE(Contrast Limited Adaptive Histogram Equalization),and Random Rotation with panoramic stitching(RRPS).Then,a method called particle swarm optimization(PSO)is used to segment tumor regions in an MR *** that,a hybrid CNN-LSTM classifier is applied to classify an image as a tumor or *** this proposed hybrid model,the CNN classifier is used for generating the feature map and the LSTM classifier is used for the classification *** effectiveness of the proposed approach is analyzed based on the different metrics and outcomes compared to different methods.

关键词： brain tumor segmentation particle swarm optimization CNN-LSTM convolution neural network

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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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A Novel Handcrafted with Deep Features Based brain tumor Diagnosis Model

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Intelligent Automation & Soft Computing 2023年第2期35卷 2057-2070页

作者： Abdul Rahaman Wahab Sait Mohamad Khairi Ishak Department of Documents and Archive Center of Documents and Administrative CommunicationKing Faisal UniversityAl Hofuf31982Al-AhsaSaudi Arabia School of Electrical and Electronic Engineering Engineering CampusUniversiti Sains Malaysia(USM)Nibong Tebal14300PenangMalaysia

In healthcare sector,image classiﬁcation is one of the crucial problems that impact the quality output from image processing *** purpose of image classiﬁcation is to categorize different healthcare images under various class labels which in turn helps in the detection and management of *** Resonance Imaging(MRI)is one of the effective non-invasive strate-gies that generate a huge and distinct number of tissue contrasts in every imaging *** technique is commonly utilized by healthcare professionals for brain tumor(BT)*** recent advancements in Machine Learning(ML)and Deep Learning(DL)models,it is possible to detect the tumor from images automatically,using a computer-aided *** current study focuses on the design of automated Deep Learning-based BT Detection and Classiﬁcation model using MRI images(DLBTDC-MRI).The proposed DLBTDC-MRI techni-que aims at detecting and classifying different stages of *** proposed DLBTDC-MRI technique involves medianﬁltering technique to remove the noise and enhance the quality of MRI ***,morphological operations-based image segmentation approach is also applied to determine the BT-affected regions in brain MRI ***,a fusion of handcrafted deep features using VGGNet is utilized to derive a valuable set of feature ***,Artiﬁcial Fish Swarm Optimization(AFSO)with Artiﬁcial Neural Network(ANN)model is utilized as a classiﬁer to decide the presence of *** order to assess the enhanced BT classiﬁcation performance of the proposed model,a comprehensive set of simulations was performed on benchmark dataset and the results were vali-dated under several measures.

关键词： brain tumor medical imaging image classiﬁcation handcrafted features deep learning parameter optimization

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An Effective Diagnosis System for brain tumor Detection and Classification

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Computer Systems Science & Engineering 2023年第8期46卷 2021-2037页

作者： Ahmed A.Alsheikhy Ahmad S.Azzahrani A.Khuzaim Alzahrani Tawfeeq Shawly Electrical Engineering Department College of EngineeringNorthern Border UniversityArar91431Saudi Arabia Department of Medical Laboratory Technology Faculty of Applied Medical SciencesNorthern Border UniversityArar91431Saudi Arabia Electrical Engineering Department Faculty of Engineering at RabighKing Abdulaziz UniversityJeddah21589Saudi Arabia

A brain tumor is an excessive development of abnormal and uncontrolled cells in the *** growth is considered deadly since it may cause *** brain controls numerous functions,such as memory,vision,and *** to the location,size,and shape of these tumors,their detection is a challenging and complex *** efforts have been conducted toward improved detection and yielded promising results and ***,the accuracy should be higher than what has been *** paper presents a method to detect brain tumors with high *** method works using an image segmentation technique and a classifier in *** utilized classifier is a SupportVector Machine(SVM).DiscreteWavelet Transform(DWT)and Principal Component Analysis(PCA)are also involved.A dataset from the Kaggle website is used to test the developed *** obtained results reached nearly 99.2%of *** paper provides a confusion matrix of applying the proposed approach to testing images and a comparative evaluation between the developed method and some works in the *** evaluation shows that the presented system outperforms other approaches regarding the accuracy,precision,and *** research discovered that the developed method is extremely useful in detecting brain tumors,given the high accuracy,precision,and recall *** proposed system directs us to believe that bringing this kind of technology to physicians diagnosing brain tumors is crucial.

关键词： brain tumor classification support vector machine artificial intelligence image segmentation tumor detection

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Computer-Aided Diagnosis Model Using Machine Learning for brain tumor Detection and Classification

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Computer Systems Science & Engineering 2023年第8期46卷 1811-1826页

作者： M.Uvaneshwari M.Baskar Department of Computer Science and Engineering School of ComputingCollege of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathurChengalpattu603203TamilnaduIndia Department of Computing Technologies School of ComputingCollege of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathurChengalpattu603203TamilnaduIndia

The brain tumor(BT)is created by an uncontrollable rise of anomalous cells in brain tissue,and it consists of 2 types of cancers they are malignant and benign *** benevolent BT does not affect the neighbouring healthy and normal tissue;however,the malignant could affect the adjacent brain tissues,which results in *** recognition of BT is highly significant to protecting the patient’s ***,the BT can be identified through the magnetic resonance imaging(MRI)scanning *** the radiotherapists are not offering effective tumor segmentation in MRI images because of the position and unequal shape of the tumor in the ***,ML has prevailed against standard image processing *** studies denote the superiority of machine learning(ML)techniques over standard ***,this study develops novel brain tumor detection and classification model using met heuristic optimization with machine learning(BTDC-MOML)*** accomplish the detection of brain tumor effectively,a Computer-Aided Design(CAD)model using Machine Learning(ML)technique is proposed in this research ***,the input image pre-processing is performed using Gaborfiltering(GF)based noise removal,contrast enhancement,and skull ***,mayfly optimization with the Kapur’s thresholding based segmentation process takes *** feature extraction proposes,local diagonal extreme patterns(LDEP)are *** last,the Extreme Gradient Boosting(XGBoost)model can be used for the BT classification *** accuracy analysis is performed in terms of Learning accuracy,and the validation accuracy is performed to determine the efficiency of the proposed research *** experimental validation of the proposed model demonstrates its promising performance over other existing methods.

关键词： brain tumor machine learning segmentation computer-aided diagnosis skull stripping

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Precise Multi-Class Classification of brain tumor via Optimization Based Relevance Vector Machine

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Intelligent Automation & Soft Computing 2023年第4期36卷 1173-1188页

作者： S.Keerthi P.Santhi Department of Computer Science Engineering Dayananda Sagar College of EngineeringBangalore560078India Department of Computer Science Engineering M.Kumarasamy College of EngineeringKarurTamil Nadu639113India

The objective of this research is to examine the use of feature selection and classiﬁcation methods for distinguishing different types of brain *** brain tumor is characterized by an anomalous proliferation of brain cells that can either be benign or *** tumors are misdiagnosed due to the variabil-ity and complexity of lesions,which reduces the survival rate in ***-sis of brain tumors via computer vision algorithms is a challenging *** and classiﬁcation of brain tumors are currently one of the most essential surgical and pharmaceutical *** brain tumor identi-ﬁcation techniques require manual segmentation or handcrafted feature extraction that is error-prone and *** the proposed research work is mainly focused on medical image processing,which takes Magnetic Resonance Imaging(MRI)images as input and performs preprocessing,segmentation,fea-ture extraction,feature selection,similarity measurement,and classiﬁcation steps for identifying brain ***,the medianﬁlter is practically applied to the input image to reduce the *** graph-cut segmentation technique is used to segment the tumor *** texture feature is extracted from the output of the segmented *** extracted feature is selected by using the Ant Colony Opti-mization(ACO)algorithm to improve the performance of the classiﬁ*** prob-abilistic approach is used to solve computing *** Euclidean distance is used to calculate the degree of similarity for each extracted *** selected feature value is given to the Relevance Vector Machine(RVM)which is a multi-class classiﬁcation ***,the tumor is classiﬁed as abnormal or *** experimental result reveals that the proposed RVM technique gives a better accuracy range of 98.87%when compared to the traditional Support Vector Machine(SVM)technique.

关键词： brain tumor segmentation classiﬁcation relevance vector machine(RVM) ant colony optimization(ACO)

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An Improved Deep Structure for Accurately brain tumor Recognition

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Computer Systems Science & Engineering 2023年第8期46卷 1597-1616页

作者： Mohamed Maher Ata Reem N.Yousef Faten Khalid Karim Doaa Sami Khafaga Department of Communications and Electronics Engineering MISR Higher Institute for Engineering and TechnologyMansoura35516Egypt Department of Electronics and Communications Delta Higher Institute for Engineering and TechnologyMansoura35516Egypt Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

brain neoplasms are recognized with a biopsy,which is not commonly done before decisive brain *** using Convolutional Neural Networks(CNNs)and textural features,the process of diagnosing brain tumors by radiologists would be a noninvasive *** paper proposes a features fusion model that can distinguish between no tumor and brain tumor types via a novel deep learning *** proposed model extracts Gray Level Co-occurrence Matrix(GLCM)textural features from MRI brain tumor ***,a deep neural network(DNN)model has been proposed to select the most salient features from the ***,it manipulates the extraction of the additional high levels of salient features from a proposed CNN ***,a fusion process has been utilized between these two types of features to form the input layer of additional proposed DNN model which is responsible for the recognition *** common datasets have been applied and tested,Br35H and FigShare *** first dataset contains binary labels,while the second one splits the brain tumor into four classes;glioma,meningioma,pituitary,and no ***,several performance metrics have been evaluated from both datasets,including,accuracy,sensitivity,specificity,F-score,and training *** results show that the proposed methodology has achieved superior performance compared with the current state of art *** proposed system has achieved about 98.22%accuracy value in the case of the Br35H dataset however,an accuracy of 98.01%has been achieved in the case of the FigShare dataset.

关键词： brain tumor convolutional neural network gray level co-occurrence matrix noninvasive FigShare dataset Br35H dataset

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Surgical site infections after elective craniotomy for brain tumor:a study on potential risk factors and related treatments

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Chinese Neurosurgical Journal 2023年第4期9卷 267-275页

作者： Yifan Lv Xiang Mao Yuxuan Deng Lanbing Yu Junsheng Chu Shuyu Hao Nan Ji Department of Neurosurgery Beijing Tiantan HospitalCapital Medical UniversityBeijing 100070China Department of Neurosurgery The First Affiliated Hospital of Anhui Medical UniversityHefei 230022AnhuiChina

Background Surgical site infection(SSI)is a common complication following craniotomy that increases morbidity,mortality,and medical *** objectives of this study were to determine the relevant risk factors associated with SSI after elective craniotomy for brain tumor and analyse the treatments for *** A retrospective nested case-control study was conducted using data from patients who underwent craniotomy for brain tumor resection at the Neurosurgical Oncology Department No.6 of Beijing Tiantan Hospital,Capital Medical University,between January 2019 and December *** factors for SSI were determined using multivariate logistic regression *** analyzed microbiological and related treatment data for different SSI *** Among 2061 patients who underwent craniotomy for brain tumor,31 had SSI(1.50%).In the multivariate logistic regression analysis,body mass index(BMI)and operative duration were identified as independent risk factors for *** most common microorganism isolated from SSIs was Staphylococcus epidermidis(22.9%),and drug sensitivity results showed that gram-positive bacteria were sensitive to linezolid,vancomycin and tigecycline,whereas gram-negative bacteria were sensitive to meropenem,cefepime and *** of the seven patients who underwent bone flap removal due to osteomyelitis were infected with gram-negative *** BMI and operative duration were identified as independent risk factors for *** mellitus,previous ratio therapy,type of incision,recurrence tumor and other risk factors were not found to be associated with the occurrence of SSI in this study.

关键词： brain tumor Elective craniotomy Neurosurgery Surgical site infection Risk factors Infection control

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Engineered hydrogels for brain tumor culture and therapy

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Bio-Design and Manufacturing 2020年第3期3卷 203-226页

作者： Jai Thakor Samad Ahadian Ali Niakan Ethan Banton Fatemeh Nasrollahi Mohammad MHasani‑Sadrabadi Ali Khademhosseini Center for Minimally Invasive Therapeutics(C‑MIT) University of California-Los Angeles570 Westwood PlazaLos AngelesCA 90095USA Department of Bioengineering University of California-Los AngelesLos AngelesCA 90095USA Department of Materials Science and Engineering University of California-Los AngelesLos AngelesCA 90095USA Weintraub Center for Reconstructive Biotechnology Division of Advanced ProsthodonticsSchool of DentistryUniversity of California-Los AngelesLos AngelesCA 90095USA Department of Radiological Sciences University of California-Los AngelesLos AngelesCA 90095USA Department of Chemical and Biomolecular Engineering University of California-Los AngelesLos AngelesCA 90095USA Terasaki Institute for Biomedical Innovation(TIBI) Los AngelesCA 90024USA

brain tumors’severity ranges from benign to highly aggressive and *** tools can assist in understanding the pathophysiology of these tumors from outside the body and facilitate development of suitable antitumoral ***,we first describe the physiology and cellular composition of brain ***,we discuss the development of threedimensional tissue models utilizing brain tumor *** particular,we highlight the role of hydrogels in providing a biomimetic support for the cells to grow into defined *** technologies,such as electrospinning and bioprinting,and advanced cellular models aim to mimic the extracellular matrix and natural cellular localization in engineered tumor ***,we review current applications and prospects of hydrogels for therapeutic purposes,such as drug delivery and co-administration with other *** further development,hydrogels can serve as a reliable option for in vitro modeling and treatment of brain tumors for translational medicine.

关键词： brain tumor Bioengineering Cancer cells Drug delivery Hydrogel

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