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Contrast Normalization Strategies in brain tumor Imaging:From Preprocessing to Classification

Computer Modeling in Engineering & Sciences 2024年第8期140卷 1539-1562页

作者： Samar M.Alqhtani Toufique A.Soomro Faisal Bin Ubaid Ahmed Ali Muhammad Irfan Abdullah A.Asiri Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Electronic Engineering The University of LarkanoLarkanaSindh75660Pakistan Computer Science Department Sukkur IBA UniversitySukkurSindh65200Pakistan Eletrical Engineering Department Sukkur IBA UniversitySukkurSindh65200Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Radiological Sciences Department College of Applied Medical SciencesNajran UniversityNajran61441Saudi Arabia

Cancer-related to the nervous system and brain tumors is a leading cause of mortality in various *** resonance imaging(MRI)and computed tomography(CT)are utilized to capture brain *** plays a crucial role in the diagnosis of brain tumors and the examination of other brain ***,manual assessment of MRI images by radiologists or experts is performed to identify brain tumors and abnormalities in the early stages for timely ***,early diagnosis of brain tumors is intricate,necessitating the use of computerized *** research introduces an innovative approach for the automated segmentation of brain tumors and a framework for classifying different regions of brain *** proposed methods consist of a pipeline with several stages:preprocessing of brain images with noise removal based on Wiener Filtering,enhancing the brain using Principal Component Analysis(PCA)to obtain well-enhanced images,and then segmenting the region of interest using the Fuzzy C-Means(FCM)clustering technique in the third *** final step involves classification using the Support Vector Machine(SVM)*** classifier is applied to various types of brain tumors,such as meningioma and pituitary tumors,utilizing the Contrast-Enhanced Magnetic Resonance Imaging(CE-MRI)*** proposed method demonstrates significantly improved contrast and validates the effectiveness of the classification framework,achieving an average sensitivity of 0.974,specificity of 0.976,accuracy of 0.979,and a Dice Score(DSC)of ***,this method exhibits a shorter processing time of 0.44 s compared to existing *** performance of this method emphasizes its significance when compared to state-of-the-art methods in terms of sensitivity,specificity,accuracy,and *** enhance the method further in the future,it is feasible to standardize the approach by incorporating a set of classifiers to increase the robustness of the brain classi

关键词： brain tumor magnetic resonance imaging principal component analysis fuzzy c-clustering support vector machine

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Intelligent Machine Learning Based brain tumor Segmentation through Multi-Layer Hybrid U-Net with CNN Feature Integration

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Computers, Materials & Continua 2024年第4期79卷 1301-1317页

作者： Sharaf J.Malebary Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityP.O.Box 344Rabigh21911Saudia Arabia

brain tumors are a pressing public health concern, characterized by their high mortality and morbidity ***, the manual segmentation of brain tumors remains a laborious and error-prone task, necessitatingthe development of more precise and efficient methodologies. To address this formidable challenge, we proposean advanced approach for segmenting brain tumorMagnetic Resonance Imaging (MRI) images that harnesses theformidable capabilities of deep learning and convolutional neural networks (CNNs). While CNN-based methodshave displayed promise in the realm of brain tumor segmentation, the intricate nature of these tumors, markedby irregular shapes, varying sizes, uneven distribution, and limited available data, poses substantial obstacles toachieving accurate semantic segmentation. In our study, we introduce a pioneering Hybrid U-Net framework thatseamlessly integrates the U-Net and CNN architectures to surmount these challenges. Our proposed approachencompasses preprocessing steps that enhance image visualization, a customized layered U-Net model tailoredfor precise segmentation, and the inclusion of dropout layers to mitigate overfitting during the training ***, we leverage the CNN mechanism to exploit contextual information within brain tumorMRI images,resulting in a substantial enhancement in segmentation *** experimental results attest to the exceptionalperformance of our framework, with accuracy rates surpassing 97% across diverse datasets, showcasing therobustness and effectiveness of our approach. Furthermore, we conduct a comprehensive assessment of ourmethod’s capabilities by evaluating various performance measures, including the sensitivity, Jaccard-index, andspecificity. Our proposed model achieved 99% accuracy. The implications of our findings are profound. Theproposed Hybrid U-Net model emerges as a highly promising diagnostic tool, poised to revolutionize brain tumorimage segmentation for radiologists and clinician

关键词： brain tumor Hybrid U-Net CLAHE transfer learning MRI images

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ARGA-Unet:Advanced U-net segmentation model using residual grouped convolution and attention mechanism for brain tumor MRI image segmentation

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虚拟现实与智能硬件（中英文） 2024年第3期6卷 203-216页

作者： Siyi XUN Yan ZHANG Sixu DUAN Mingwei WANG Jiangang CHEN Tong TONG Qinquan GAO Chantong LAM Menghan HU Tao TAN Faculty of Applied Sciences Macao Polytechnic UniversityMacao 999078China Jinan Branch of China Telecom Co.Ltd. Jinan 250000China School of Physics and Electronics Shandong Normal UniversityJinan 250000China Department of Dardiovascular Medicine Affiliated Hospital of Hangzhou Normal UniversityHangzhou 310000China Clinical School of Medicine Hangzhou Normal UniversityHangzhou 310000China Hangzhou Institute of Cardiovascular Diseases Hangzhou 310000China Shanghai Key Laboratory of Multidimensional Information Processing Shanghai 200000China School of Communication&Electronic Engineering East China Normal UniversityShanghai 200000China Engineering Research Center of Traditional Chinese Medicine Intelligent Rehabilitation Ministry of EducationShanghai 200000China College of Physics and Information Engineering Fuzhou UniversityFuzhou 350000China

Background Magnetic resonance imaging(MRI)has played an important role in the rapid growth of medical imaging diagnostic technology,especially in the diagnosis and treatment of brain tumors owing to its non invasive characteristics and superior soft tissue ***,brain tumors are characterized by high non uniformity and non-obvious boundaries in MRI images because of their invasive and highly heterogeneous *** addition,the labeling of tumor areas is time-consuming and *** To address these issues,this study uses a residual grouped convolution module,convolutional block attention module,and bilinear interpolation upsampling method to improve the classical segmentation network *** influence of network normalization,loss function,and network depth on segmentation performance is further *** In the experiments,the Dice score of the proposed segmentation model reached 97.581%,which is 12.438%higher than that of traditional U-net,demonstrating the effective segmentation of MRI brain tumor *** In conclusion,we use the improved U-net network to achieve a good segmentation effect of brain tumor MRI images.

关键词： brain tumor MRI U-net Segmentation Attention mechanism Deep learning

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Extended Deep Learning Algorithm for Improved brain tumor Diagnosis System

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Intelligent Automation & Soft Computing 2024年第1期39卷 33-55页

作者： M.Adimoolam K.Maithili N.M.Balamurugan R.Rajkumar S.Leelavathy Raju Kannadasan Mohd Anul Haq Ilyas Khan ElSayed M.Tag El Din Arfat Ahmad Khan Department of Computer Engineering Government Polytechnic CollegeSevvapetTiruvallurTamil Nadu602025India Department of Computer Science and Engineering KG Reddy College of Engineering and TechnologyHyderabadTelangana500075India Department of Computer Science and Engineering Sri Venkateswara College of EngineeringSriperumbudurChennai602117India Department of Computer Science and Engineering Annamalai UniversityChidambaramTamil Nadu608002India Department of Computer Science and Engineering Aarupadai Veedu Institute of TechnologyVinayaka Mission Research FoundationPaiyanurTamil Nadu603104India Department of Electrical and Electronics Engineering Sri Venkateswara College of EngineeringSriperumbudur602117India Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Mathematics College of Science Al-ZulfiMajmaah UniversityAl-Majmaah11952Saudi Arabia Electrical Engineering Department Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer Science College of ComputingKhon Kaen UniversityKhon Kaen40002Thailand

At present,the prediction of brain tumors is performed using Machine Learning(ML)and Deep Learning(DL)*** various ML and DL algorithms are adapted to predict brain tumors to some range,some concerns still need enhancement,particularly accuracy,sensitivity,false positive and false negative,to improve the brain tumor prediction system ***,this work proposed an Extended Deep Learning Algorithm(EDLA)to measure performance parameters such as accuracy,sensitivity,and false positive and false negative *** addition,these iterated measures were analyzed by comparing the EDLA method with the Convolutional Neural Network(CNN)way further using the SPSS tool,and respective graphical illustrations were *** results were that the mean performance measures for the proposed EDLA algorithm were calculated,and those measured were accuracy(97.665%),sensitivity(97.939%),false positive(3.012%),and false negative(3.182%)for ten *** in the case of the CNN,the algorithm means accuracy gained was 94.287%,mean sensitivity 95.612%,mean false positive 5.328%,and mean false negative 4.756%.These results show that the proposed EDLA method has outperformed existing algorithms,including CNN,and ensures symmetrically improved *** EDLA algorithm introduces novelty concerning its performance and particular activation *** proposed method will be utilized effectively in brain tumor detection in a precise and accurate *** algorithm would apply to brain tumor diagnosis and be involved in various medical diagnoses *** the quantity of dataset records is enormous,then themethod’s computation power has to be updated.

关键词： brain tumor extended deep learning algorithm convolution neural network tumor detection deep learning

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brain tumor Detection and Segmentation Using RCNN

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Computers, Materials & Continua 2022年第6期71卷 5005-5020页

作者： Maham Khan Syed Adnan Shah Tenvir Ali Quratulain Aymen Khan Gyu Sang Choi Department of Computer Science University of Engineering&TechnologyTaxila47080Pakistan Faculty of Computing The Islamia University of BahawalpurBahawalpur63100Pakistan Department of Information and Communication Engineering Yeungnam UniversityGyeongbuk38541Korea

brain tumors are considered as most fatal *** reduce the risk of death,early identification of the disease is *** of the best available methods to evaluate brain tumors is Magnetic resonance Images(MRI).brain tumor detection and segmentation are tough as brain tumors may vary in size,shape,and *** makes manual detection of brain tumors by exploring MRI a tedious job for radiologists and doctors’.So an automated brain tumor detection and segmentation is *** work suggests a Region-based Convolution Neural Network(RCNN)approach for automated brain tumor identification and segmentation using MR images,which helps solve the difficulties of brain tumor identification efficiently and *** methodology is based on the accurate and efficient selection of tumorous *** reduces computational complexity and *** have validated the designed experimental setup on a standard dataset,BraTS *** used binary evaluation matrices based on Dice Similarity Coefficient(DSC)and Mean Average Precision(mAP).The segmentation results are compared with state-of-the-art methodologies to demonstrate the effectiveness of the proposed *** suggested approach attained an averageDSC of 0.92 andmAP 0.92 for 10 patients,while on the whole dataset,the scores are DSC 0.89 and mAP *** following results clearly show the performance efficiency of the proposed methodology.

关键词： brain tumor MRI preprocessing image segmentation brain tumor localization medical ML RCNN BraTS 2020 LGG HGG

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brain tumor Segmentation using Multi-View Attention based Ensemble Network

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Computers, Materials & Continua 2022年第9期72卷 5793-5806页

作者： Noreen Mushtaq Arfat Ahmad Khan Faizan Ahmed Khan Muhammad Junaid Ali Malik Muhammad Ali Shahid Chitapong Wechtaisong Peerapong Uthansakul COMSATS University Islamabad Islamabad Campus45550Pakistan Suranaree University of Technology Nakhon Ratchasima30000Thailand COMSATS University Islamabad Lahore Campus54000Pakistan Virtual University of Pakistan Islamabad Campus45550Pakistan COMSATS University Islamabad Vehari Campus61100Pakistan

Astrocytoma IV or glioblastoma is one of the fatal and dangerous types of brain *** detection of brain tumor increases the survival rate and helps in reducing the fatality *** imaging modalities have been used for diagnosing by expert radiologists,and Medical Resonance Image(MRI)is considered a better option for detecting brain tumors as MRI is a non-invasive technique and provides better visualization of the brain *** of the challenging issues is to identify the tumorous region from the MRI scans *** segmentation is performed by medical experts,which is a time-consuming task and got chances of *** overcome this issue,automatic segmentation is performed for quick and accurate *** proposed approach is to capture inter-slice information and reduce the *** learning-based brain tumor segmentation techniques proved best among available segmentation ***,deep learning may miss some preliminary info while using MRI images during *** MRI volumes are volumetric,3D U-Net-based models are used but *** of multiple 2D U-Net predictions in axial,sagittal,and coronal views help to capture inter-slice *** approach may reduce the system ***,the Conditional Random Fields(CRF)reduce the predictions’false positives and improve the segmentation *** model is applied to brain tumor Segmentation(BraTS)2019 dataset,and cross-validation is performed to check the accuracy of *** proposed approach achieves Dice Similarity Score(DSC)of 0.77 on Enhancing tumor(ET),0.90 on Whole tumor(WT),and 0.84 on tumor Core(TC)with reduced Hausdorff Distance(HD)of 3.05 on ET,5.12 on WT and 3.89 on TC.

关键词： brain tumor deep learning detection conditional random field segmentation

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brain tumor Diagnosis Using Sparrow Search Algorithm Based Deep Learning Model

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Computer Systems Science & Engineering 2023年第2期44卷 1793-1806页

作者： G.Ignisha Rajathi R.Ramesh Kumar D.Ravikumar T.Joel Seifedine Kadry Chang-Won Jeong Yunyoung Nam Department of Computer Science and Business Systems Sri Krishna College of Engineering and TechnologyCoimbatore641008India Department of Information Technology Sri Krishna College of TechnologyCoimbatore641008India Department of Electronics and Communication Engineering Kings Engineering CollegeChennai602117India Department of Electronics and Communication Engineering R.M.K.Engineering CollegeChennai601206India Deparmtent of Applied Data Science Noroff University CollegeKristiansandNorway Medical Convergence Research Center Wonkwang UniversityIksanKorea Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea

Recently,Internet of Medical Things(IoMT)has gained considerable attention to provide improved healthcare services to *** earlier diag-nosis of brain tumor(BT)using medical imaging becomes an essential task,auto-mated IoMT and cloud enabled BT diagnosis model can be devised using recent deep learning *** this motivation,this paper introduces a novel IoMT and cloud enabled BT diagnosis model,named *** IoMTC-HDBT model comprises the data acquisition process by the use of IoMT devices which captures the magnetic resonance imaging(MRI)brain images and transmit them to the cloud ***,adaptive windowﬁltering(AWF)based image preprocessing is used to remove *** addition,the cloud server executes the disease diagnosis model which includes the sparrow search algorithm(SSA)with GoogleNet(SSA-GN)*** IoMTC-HDBT model applies functional link neural network(FLNN),which has the ability to detect and classify the MRI brain images as normal or ***ﬁnds useful to generate the reports instantly for patients located in remote *** validation of the IoMTC-HDBT model takes place against BRATS2015 Challenge dataset and the experimental analysis is car-ried out interms of sensitivity,accuracy,and speciﬁ*** experimentation out-come pointed out the betterment of the proposed model with the accuracy of 0.984.

关键词： Internet of medical things healthcare brain tumor disease classiﬁcation deep learning metaheuristics

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brain tumor Identification Using Data Augmentation and Transfer Learning Approach

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Computer Systems Science & Engineering 2023年第8期46卷 1845-1861页

作者： K.Kavin Kumar P.M.Dinesh P.Rayavel L.Vijayaraja R.Dhanasekar Rupa Kesavan Kannadasan Raju Arfat Ahmad Khan Chitapong Wechtaisong Mohd Anul Haq Zamil S.Alzamil Ahmed Alhussen Department of Electronics and Communication Engineering Kongu Engineering College638060Tamil NaduIndia Department of Electronics and Communication Engineering Sona College of Technology636005Tamil NaduIndia Department of Computer Science and Engineering Sri Sairam Institute of TechnologyChennai600044Tamil NaduIndia Department of Electrical and Electronics Engineering Sri Sairam Institute of TechnologyChennai600044Tamil NaduIndia Department of Computer Science and Engineering Sri Venkateswara College of EngineeringSriperumbudur602117Tamil NaduIndia Department of Electrical and Electronics Engineering Sri Venkateswara College of EngineeringSriperumbudur602117Tamil NaduIndia Department of Computer Science College of ComputingKhon Kaen UniversityKhonKaen40002Thailand School of Telecommunication Engineering Suranaree University of TechnologyNakhon Ratchasima30000Thailand Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia

A brain tumor is a lethal neurological disease that affects the average performance of the brain and can be *** India,around 15 million cases are diagnosed *** mitigate the seriousness of the tumor it is essential to diagnose at the ***,the manual evaluation process utilizing Magnetic Resonance Imaging(MRI)causes a few worries,remarkably inefficient and inaccurate brain tumor ***,the examination process of brain tumors is intricate as they display high unbalance in nature like shape,size,appearance,and ***,a precise and expeditious prognosis of brain tumors is essential for implementing the of an implicit *** computer models adapted to diagnose the tumor,but the accuracy of the model needs to be *** all the above mentioned things,this work aims to identify the best classification system by considering the prediction accuracy out of Alex-Net,ResNet 50,and Inception *** augmentation is performed on the database and fed into the three convolutions neural network(CNN)models.A comparison line is drawn between the three models based on accuracy and *** accuracy of 96.2%is obtained for AlexNet with augmentation and performed better than ResNet 50 and Inception V3 for the 120th *** the suggested model with higher accuracy,it is highly reliable if brain tumors are diagnosed with available datasets.

关键词： AlexNet brain tumor data augmentation inception V3 ResNet 50

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brain tumor:Hybrid Feature Extraction Based on UNet and 3DCNN

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Computer Systems Science & Engineering 2023年第5期45卷 2093-2109页

作者： Sureshkumar Rajagopal Tamilvizhi Thanarajan Youseef Alotaibi Saleh Alghamdi Center for System Design Chennai Institute of TechnologyChennai600069India Department of Information Technology Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering CollegeChennai600062India Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkah21955Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

Automated segmentation of brain tumors using Magnetic Resonance Imaging(MRI)data is critical in the analysis and monitoring of disease *** a result,gliomas are aggressive and diverse tumors that may be split into intra-tumoral groups by using effective and accurate segmentation *** is intended to extract characteristics from an image using the Gray Level Co-occurrence(GLC)matrix feature extraction method described in the proposed *** Convolutional Neural Networks(CNNs),which are commonly used in biomedical image segmentation,CNNs have significantly improved the precision of the state-of-the-art segmentation of a brain *** two segmentation networks,a U-Net and a 3D CNN,we present a major yet easy combinative technique that results in improved and more precise *** U-Net and 3D CNN are used together in this study to get better and more accurate estimates of what is going *** the dataset,two models were developed and assessed to provide segmentation maps that differed fundamentally in terms of the segmented tumour ***,the estimates was made by two separate models that were put together to produce the final *** comparison to current state-of-the-art designs,the precision(percentage)was 98.35,98.5,and 99.4 on the validation set for tumor core,enhanced tumor,and whole tumor,respectively.

关键词： Medical imaging segmentation U-net 3D CNN brain tumor

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Effectiveness of Deep Learning Models for brain tumor Classification and Segmentation

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Computers, Materials & Continua 2023年第7期76卷 711-729页

作者： Muhammad Irfan Ahmad Shaf Tariq Ali Umar Farooq Saifur Rahman Salim Nasar Faraj Mursal Mohammed Jalalah Samar M.Alqhtani Omar AlShorman Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Department of Computer Science COMSATS University IslamabadSahiwal CampusSahiwal57000Pakistan Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia College of Engineering Najran UniversityNajran61441Saudi Arabia

A brain tumor is a mass or growth of abnormal cells in the *** children and adults,brain tumor is considered one of the leading causes of *** are several types of brain tumors,including benign(non-cancerous)and malignant(cancerous)*** brain tumors as early as possible is essential,as this can improve the chances of successful treatment and *** this problem,we bring forth a hybrid intelligent deep learning technique that uses several pre-trained models(Resnet50,Vgg16,Vgg19,U-Net)and their integration for computer-aided detection and localization systems in brain *** pre-trained and integrated deep learning models have been used on the publicly available dataset from The Cancer Genome *** dataset consists of 120 *** pre-trained models have been used to classify tumor or no tumor images,while integrated models are applied to segment the tumor region *** have evaluated their performance in terms of loss,accuracy,intersection over union,Jaccard distance,dice coefficient,and dice coefficient *** pre-trained models,the U-Net model achieves higher performance than other models by obtaining 95%*** contrast,U-Net with ResNet-50 out-performs all other models from integrated pre-trained models and correctly classified and segmented the tumor region.

关键词： brain tumor deep learning ensemble detection healthcare

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