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

Brain tumors are a pressing public health concern, characterized by their high mortality and morbidity rates.Nevertheless, 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 process.Additionally, we leverage the CNN mechanism to exploit contextual information within brain tumorMRI images,resulting in a substantial enhancement in segmentation accuracy.Our 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 clinicians.

Targeting brain tumors with innovative nanocarriers:bridging the gap through the blood-brain barrier

Background:Glioblastoma multiforme(GBM)is recognized as the most lethal and most highly invasive tumor.The high likelihood of treatment failure arises fromthe presence of the blood-brain barrier(BBB)and stemcells around GBM,which avert the entry of chemotherapeutic drugs into the tumormass.Objective:Recently,several researchers have designed novel nanocarrier systems like liposomes,dendrimers,metallic nanoparticles,nanodiamonds,and nanorobot approaches,allowing drugs to infiltrate the BBB more efficiently,opening up innovative avenues to prevail over therapy problems and radiation therapy.Methods:Relevant literature for this manuscript has been collected from a comprehensive and systematic search of databases,for example,PubMed,Science Direct,Google Scholar,and others,using specific keyword combinations,including“glioblastoma,”“brain tumor,”“nanocarriers,”and several others.Conclusion:This review also provides deep insights into recent advancements in nanocarrier-based formulations and technologies for GBM management.Elucidation of various scientific advances in conjunction with encouraging findings concerning the future perspectives and challenges of nanocarriers for effective brain tumor management has also been discussed.

Contrast Normalization Strategies in Brain Tumor Imaging:From Preprocessing to Classification

Cancer-related to the nervous system and brain tumors is a leading cause of mortality in various countries.Magnetic resonance imaging(MRI)and computed tomography(CT)are utilized to capture brain images.MRI plays a crucial role in the diagnosis of brain tumors and the examination of other brain disorders.Typically,manual assessment of MRI images by radiologists or experts is performed to identify brain tumors and abnormalities in the early stages for timely intervention.However,early diagnosis of brain tumors is intricate,necessitating the use of computerized methods.This research introduces an innovative approach for the automated segmentation of brain tumors and a framework for classifying different regions of brain tumors.The 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 step.The final step involves classification using the Support Vector Machine(SVM)classifier.The classifier is applied to various types of brain tumors,such as meningioma and pituitary tumors,utilizing the Contrast-Enhanced Magnetic Resonance Imaging(CE-MRI)database.The 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 0.957.Additionally,this method exhibits a shorter processing time of 0.44 s compared to existing approaches.The performance of this method emphasizes its significance when compared to state-of-the-art methods in terms of sensitivity,specificity,accuracy,and DSC.To 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 classification method.

L_(1)-Smooth SVM with Distributed Adaptive Proximal Stochastic Gradient Descent with Momentum for Fast Brain Tumor Detection

Brain tumors come in various types,each with distinct characteristics and treatment approaches,making manual detection a time-consuming and potentially ambiguous process.Brain tumor detection is a valuable tool for gaining a deeper understanding of tumors and improving treatment outcomes.Machine learning models have become key players in automating brain tumor detection.Gradient descent methods are the mainstream algorithms for solving machine learning models.In this paper,we propose a novel distributed proximal stochastic gradient descent approach to solve the L_(1)-Smooth Support Vector Machine(SVM)classifier for brain tumor detection.Firstly,the smooth hinge loss is introduced to be used as the loss function of SVM.It avoids the issue of nondifferentiability at the zero point encountered by the traditional hinge loss function during gradient descent optimization.Secondly,the L_(1) regularization method is employed to sparsify features and enhance the robustness of the model.Finally,adaptive proximal stochastic gradient descent(PGD)with momentum,and distributed adaptive PGDwithmomentum(DPGD)are proposed and applied to the L_(1)-Smooth SVM.Distributed computing is crucial in large-scale data analysis,with its value manifested in extending algorithms to distributed clusters,thus enabling more efficient processing ofmassive amounts of data.The DPGD algorithm leverages Spark,enabling full utilization of the computer’s multi-core resources.Due to its sparsity induced by L_(1) regularization on parameters,it exhibits significantly accelerated convergence speed.From the perspective of loss reduction,DPGD converges faster than PGD.The experimental results show that adaptive PGD withmomentumand its variants have achieved cutting-edge accuracy and efficiency in brain tumor detection.Frompre-trained models,both the PGD andDPGD outperform other models,boasting an accuracy of 95.21%.

Multi-Level Parallel Network for Brain Tumor Segmentation

Accurate automatic segmentation of gliomas in various sub-regions,including peritumoral edema,necrotic core,and enhancing and non-enhancing tumor core from 3D multimodal MRI images,is challenging because of its highly heterogeneous appearance and shape.Deep convolution neural networks(CNNs)have recently improved glioma segmentation performance.However,extensive down-sampling such as pooling or stridden convolution in CNNs significantly decreases the initial image resolution,resulting in the loss of accurate spatial and object parts information,especially information on the small sub-region tumors,affecting segmentation performance.Hence,this paper proposes a novel multi-level parallel network comprising three different level parallel subnetworks to fully use low-level,mid-level,and high-level information and improve the performance of brain tumor segmentation.We also introduce the Combo loss function to address input class imbalance and false positives and negatives imbalance in deep learning.The proposed method is trained and validated on the BraTS 2020 training and validation dataset.On the validation dataset,ourmethod achieved a mean Dice score of 0.907,0.830,and 0.787 for the whole tumor,tumor core,and enhancing tumor core,respectively.Compared with state-of-the-art methods,the multi-level parallel network has achieved competitive results on the validation dataset.

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

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 contrast.However,brain tumors are characterized by high non uniformity and non-obvious boundaries in MRI images because of their invasive and highly heterogeneous nature.In addition,the labeling of tumor areas is time-consuming and laborious.Methods 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 U-net.The influence of network normalization,loss function,and network depth on segmentation performance is further considered.Results 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 images.Conclusions In conclusion,we use the improved U-net network to achieve a good segmentation effect of brain tumor MRI images.

Extended Deep Learning Algorithm for Improved Brain Tumor Diagnosis System

At present,the prediction of brain tumors is performed using Machine Learning(ML)and Deep Learning(DL)algorithms.Although 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 symmetrically.Therefore,this work proposed an Extended Deep Learning Algorithm(EDLA)to measure performance parameters such as accuracy,sensitivity,and false positive and false negative rates.In 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 shown.The 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 iterations.Whereas 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 parameters.Thus EDLA algorithm introduces novelty concerning its performance and particular activation function.This proposed method will be utilized effectively in brain tumor detection in a precise and accurate manner.This algorithm would apply to brain tumor diagnosis and be involved in various medical diagnoses aftermodification.If the quantity of dataset records is enormous,then themethod’s computation power has to be updated.

Transformation of MRI Images to Three-Level Color Spaces for Brain Tumor Classification Using Deep-Net

In the domain ofmedical imaging,the accurate detection and classification of brain tumors is very important.This study introduces an advanced method for identifying camouflaged brain tumors within images.Our proposed model consists of three steps:Feature extraction,feature fusion,and then classification.The core of this model revolves around a feature extraction framework that combines color-transformed images with deep learning techniques,using the ResNet50 Convolutional Neural Network(CNN)architecture.So the focus is to extract robust feature fromMRI images,particularly emphasizingweighted average features extracted fromthe first convolutional layer renowned for their discriminative power.To enhance model robustness,we introduced a novel feature fusion technique based on the Marine Predator Algorithm(MPA),inspired by the hunting behavior of marine predators and has shown promise in optimizing complex problems.The proposed methodology can accurately classify and detect brain tumors in camouflage images by combining the power of color transformations,deep learning,and feature fusion via MPA,and achieved an accuracy of 98.72%on a more complex dataset surpassing the existing state-of-the-art methods,highlighting the effectiveness of the proposed model.The importance of this research is in its potential to advance the field ofmedical image analysis,particularly in brain tumor diagnosis,where diagnoses early,and accurate classification are critical for improved patient results.

An Adapted Convolutional Neural Network for Brain Tumor Detection

In medical imaging, particularly for analyzing brain tumor MRIs, the expertise of skilled neurosurgeons or radiologists is often essential. However, many developing countries face a significant shortage of these specialists, which impedes the accurate identification and analysis of tumors. This shortage exacerbates the challenge of delivering precise and timely diagnoses and delays the production of comprehensive MRI reports. Such delays can critically affect treatment outcomes, especially for conditions requiring immediate intervention, potentially leading to higher mortality rates. In this study, we introduced an adapted convolutional neural network designed to automate brain tumor diagnosis. Our model features fewer layers, each optimized with carefully selected hyperparameters. As a result, it significantly reduced both execution time and memory usage compared to other models. Specifically, its execution time was 10 times shorter than that of the referenced models, and its memory consumption was 3 times lower than that of ResNet. In terms of accuracy, our model outperformed all other architectures presented in the study, except for ResNet, which showed similar performance with an accuracy of around 90%.

Brain Tumor Detection and Segmentation Using RCNN

Brain tumors are considered as most fatal cancers.To reduce the risk of death,early identification of the disease is required.One 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 location.That 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 required.This 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 accurately.Our methodology is based on the accurate and efficient selection of tumorous areas.That reduces computational complexity and time.We have validated the designed experimental setup on a standard dataset,BraTS 2020.We 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 method.The 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 0.90.The following results clearly show the performance efficiency of the proposed methodology.

Differentiation profile of brain tumor stem cells:a comparative study with neural stem cells

Understanding of the differentiation profile of brain tumor stem cells （BTSCs）, the key ones among tumor cell population, through comparison with neural stem cells （NSCs） would lend insight into the origin of glioma and ultimately yield new approaches to fight this intractable disease. Here, we cultured and purified BTSCs from surgical glioma specimens and NSCs from human fetal brain tissue, and further analyzed their cellular biological behaviors, especially their differentiation property. As expected, NSCs differentiated into mature neural phenotypes. In the same differentiation condition, however, BTSCs exhibited distinguished differences. Morphologically, cells grew flattened and attached for the first week, but gradually aggregated and reformed floating tumor sphere thereafter. During the corresponding period, the expression rate of undifferentiated cell marker CD 133 and nestin in BTSCs kept decreasing, but 1 week later, they regained ascending tendency. Interestingly, the differentiated cell markers GFAP and β-tubulinlII showed an expression change inverse to that of undifferentiated cell markers. Taken together, BTSCs were revealed to possess a capacity to resist differentiation, which actually represents the malignant behaviors of glioma.

MRI Brain Tumor Segmentation Using 3D U-Net with Dense Encoder Blocks and Residual Decoder Blocks

The main task of magnetic resonance imaging (MRI) automatic brain tumor segmentation is to automaticallysegment the brain tumor edema, peritumoral edema, endoscopic core, enhancing tumor core and nonenhancingtumor core from 3D MR images. Because the location, size, shape and intensity of brain tumors vary greatly, itis very difficult to segment these brain tumor regions automatically. In this paper, by combining the advantagesof DenseNet and ResNet, we proposed a new 3D U-Net with dense encoder blocks and residual decoder blocks.We used dense blocks in the encoder part and residual blocks in the decoder part. The number of output featuremaps increases with the network layers in contracting path of encoder, which is consistent with the characteristicsof dense blocks. Using dense blocks can decrease the number of network parameters, deepen network layers,strengthen feature propagation, alleviate vanishing-gradient and enlarge receptive fields. The residual blockswere used in the decoder to replace the convolution neural block of original U-Net, which made the networkperformance better. Our proposed approach was trained and validated on the BraTS2019 training and validationdata set. We obtained dice scores of 0.901, 0.815 and 0.766 for whole tumor, tumor core and enhancing tumorcore respectively on the BraTS2019 validation data set. Our method has the better performance than the original3D U-Net. The results of our experiment demonstrate that compared with some state-of-the-art methods, ourapproach is a competitive automatic brain tumor segmentation method.

Engineered hydrogels for brain tumor culture and therapy

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

Brain Tumor Segmentation using Multi-View Attention based Ensemble Network

Astrocytoma IV or glioblastoma is one of the fatal and dangerous types of brain tumors.Early detection of brain tumor increases the survival rate and helps in reducing the fatality rate.Various 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 region.One of the challenging issues is to identify the tumorous region from the MRI scans correctly.Manual segmentation is performed by medical experts,which is a time-consuming task and got chances of errors.To overcome this issue,automatic segmentation is performed for quick and accurate results.The proposed approach is to capture inter-slice information and reduce the outliers.Deep learning-based brain tumor segmentation techniques proved best among available segmentation techniques.However,deep learning may miss some preliminary info while using MRI images during segmentation.As MRI volumes are volumetric,3D U-Net-based models are used but complex.Combinations of multiple 2D U-Net predictions in axial,sagittal,and coronal views help to capture inter-slice information.This approach may reduce the system complexity.Moreover,the Conditional Random Fields(CRF)reduce the predictions’false positives and improve the segmentation results.This model is applied to Brain Tumor Segmentation(BraTS)2019 dataset,and cross-validation is performed to check the accuracy of results.The 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.

Application of Intraoperative Contrast-Enhanced Ultrasound in the Resection of Brain Tumors

Objective:To investigate the value of routine intraoperative ultrasound(IU)and intraoperative contrast-enhanced ultrasound(ICEUS)in the surgical treatment of brain tumors,and to explore the utilization of ICEUS for the removal of the remnants surrounding the resection cavity.Methods:In total,51 patients who underwent operations from 2012 to 2018 due to different tumors in the brain were included in this study.The clinical data were evaluated retrospectively.IU was performed in all patients,among which 28 patients underwent ICEUS.The effects of IU and ICEUS on tumor resection and recurrence were evaluated.

Precision radiotherapy for brain tumors A 10-year bibliometric analysis

OBJECTIVE： Precision radiotherapy plays an important role in the management of brain tumors. This study aimed to identify global research trends in precision radiotherapy for brain tumors using a bibliometric analysis of the Web of Science. DATA RETRIEVAL： We performed a bibliometric analysis of data retrievals for precision radiotherapy for brain tumors containing the key words cerebral tumor, brain tumor, intensity-modulated radiotherapy, stereotactic body radiation therapy, stereotactic ablative radiotherapy, imaging-guided radiotherapy, dose-guided radiotherapy, stereotactic brachytherapy, and stereotactic radiotherapy using the Web of Science. SELECTION CRITERIA： Inclusion criteria： （a） peer-reviewed articles on precision radiotherapy for brain tumors which were published and indexed in the Web of Science; （b） type of articles： original research articles and reviews; （c） year of publication： 2002-2011. Exclusion criteria： （a） articles that required manual searching or telephone access; （b） Corrected papers or book chapters. MAIN OUTCOME MEASURES： （1） Annual publication output; （2） distribution according to country; （3） distribution according to institution; （4） top cited publications; （5） distribution according to journals; and （6） comparison of study results on precision radiotherapy for brain tumors. RESULTS： The stereotactic radiotherapy, intensity-modulated radiotherapy, and imaging-guided radiotherapy are three major methods of precision radiotherapy for brain tumors. There were 260 research articles addressing precision radiotherapy for brain tumors found within the Web of Science. The USA published the most papers on precision radiotherapy for brain tumors, followed by Germany and France. European Synchrotron Radiation Facility, German Cancer Research Center and Heidelberg University were the most prolific research institutes for publications on precision radiotherapy for brain tumors. Among the top 13 research institutes publishing in this field, seven are in the USA, three are in Germany, two are in France, and there is one institute in India. Research interests including urology and nephrology, clinical neurology, as well as rehabilitation are involved in precision radiotherapy for brain tumors studies. CONCLUSION： Precision radiotherapy for brain tumors remains a highly active area of research and development.

Home visits in brain tumor patient: how nurse and family members cooperate in tumor patient's family self-care

Purposes： We reported the roles and fimctions of nurses in home visits for brain tumor patients using the family health assessment guide in the study. Methods： One patient of brain glioma was chosen as the case illustration. The nurses assessed the patients＇ situation, their families and living environment individually. All these factors were analyzed together. Results： The nurses then implemented their knowledge and skills to adopt different measures in different conditions, investigated the patients＇ health problems and carried out personalized effective actions. Conclusions： Nurses should put effort into community nursing to allow patients to live in a safe environment, to satisfy the health needs of human being and their needs for health knowledge, and enhance their self-care abilities.

Cytomegalovirus in human brain tumors:Role in pathogenesis and potential treatment options

During the last years increasing evidence implies that human cytomegalovirus(CMV) can be attributed to human malignancies arising from numerous tissues. In this perspective, we will review and discuss the potential mechanisms through which CMV infection may contribute to brain tumors by affecting tumor cell initiation, progression and metastasis formation. Recent evidence also suggests that anti-CMV treatment results in impaired tumor growth of CMV positive xenografts in animal models and potentially increased survival in CMV positive glioblastoma patients. Based on these observations and the high tumor promoting capacity of this virus, the classical and novel antiviral therapies against CMV should be revisited as they may represent a great promise for halting tumor progression and lower cancer deaths.

A Learning Based Brain Tumor Detection System

Brain tumor is one of the most dangerous disease that causes due to uncontrollable and abnormal cell partition.In this paper,we have used MRI brain scan in comparison with CT brain scan as it is less harmful to detect brain tumor.We considered watershed segmentation technique for brain tumor detection.The proposed methodology is divided as follows:pre-processing,computing foreground applying watershed,extract and supply features to machine learning algorithms.Consequently,this study is tested on big data set of images and we achieved acceptable accuracy from K-NN classification algorithm in detection of brain tumor.

An Ensemble of Optimal Deep Learning Features for Brain Tumor Classification

Owing to technological developments,Medical image analysis has received considerable attention in the rapid detection and classification of diseases.The brain is an essential organ in humans.Brain tumors cause loss of memory,vision,and name.In 2020,approximately 18,020 deaths occurred due to brain tumors.These cases can be minimized if a brain tumor is diagnosed at a very early stage.Computer vision researchers have introduced several techniques for brain tumor detection and classification.However,owing to many factors,this is still a challenging task.These challenges relate to the tumor size,the shape of a tumor,location of the tumor,selection of important features,among others.In this study,we proposed a framework for multimodal brain tumor classification using an ensemble of optimal deep learning features.In the proposed framework,initially,a database is normalized in the form of high-grade glioma(HGG)and low-grade glioma(LGG)patients and then two pre-trained deep learning models(ResNet50 and Densenet201)are chosen.The deep learning models were modified and trained using transfer learning.Subsequently,the enhanced ant colony optimization algorithm is proposed for best feature selection from both deep models.The selected features are fused using a serial-based approach and classified using a cubic support vector machine.The experimental process was conducted on the BraTs2019 dataset and achieved accuracies of 87.8%and 84.6%for HGG and LGG,respectively.The comparison is performed using several classification methods,and it shows the significance of our proposed technique.