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.

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.

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.

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.

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%.

Tumor necrosis factor-stimulated gene-6 ameliorates early brain injury after subarachnoid hemorrhage by suppressing NLRC4 inflammasome-mediated astrocyte pyroptosis

Subarachnoid hemorrhage is associated with high morbidity and mortality and lacks effective treatment.Pyroptosis is a crucial mechanism underlying early brain injury after subarachnoid hemorrhage.Previous studies have confirmed that tumor necrosis factor-stimulated gene-6(TSG-6)can exert a neuroprotective effect by suppressing oxidative stress and apoptosis.However,no study to date has explored whether TSG-6 can alleviate pyroptosis in early brain injury after subarachnoid hemorrhage.In this study,a C57BL/6J mouse model of subarachnoid hemorrhage was established using the endovascular perforation method.Our results indicated that TSG-6 expression was predominantly detected in astrocytes,along with NLRC4 and gasdermin-D(GSDMD).The expression of NLRC4,GSDMD and its N-terminal domain(GSDMD-N),and cleaved caspase-1 was significantly enhanced after subarachnoid hemorrhage and accompanied by brain edema and neurological impairment.To explore how TSG-6 affects pyroptosis during early brain injury after subarachnoid hemorrhage,recombinant human TSG-6 or a siRNA targeting TSG-6 was injected into the cerebral ventricles.Exogenous TSG-6 administration downregulated the expression of NLRC4 and pyroptosis-associated proteins and alleviated brain edema and neurological deficits.Moreover,TSG-6 knockdown further increased the expression of NLRC4,which was accompanied by more severe astrocyte pyroptosis.In summary,our study revealed that TSG-6 provides neuroprotection against early brain injury after subarachnoid hemorrhage by suppressing NLRC4 inflammasome activation-induced astrocyte pyroptosis.

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.

RBEBT: A ResNet-Based BA-ELM for Brain Tumor Classification

Brain tumor refers to the formation of abnormal cells in the brain.It can be divided into benign and malignant.The main diagnostic methods for brain tumors are plain X-ray film,Magnetic resonance imaging(MRI),and so on.However,these artificial diagnosis methods are easily affected by external factors.Scholars have made such impressive progress in brain tumors classification by using convolutional neural network(CNN).However,there are still some problems:(i)There are many parameters in CNN,which require much calculation.(ii)The brain tumor data sets are relatively small,which may lead to the overfitting problem in CNN.In this paper,our team proposes a novel model(RBEBT)for the automatic classification of brain tumors.We use fine-tuned ResNet18 to extract the features of brain tumor images.The RBEBT is different from the traditional CNN models in that the randomized neural network(RNN)is selected as the classifier.Meanwhile,our team selects the bat algorithm(BA)to opti7mize the parameters of RNN.We use fivefold cross-validation to verify the superiority of the RBEBT.The accuracy(ACC),specificity(SPE),precision(PRE),sensitivity(SEN),and F1-score(F1)are 99.00%,95.00%,99.00%,100.00%,and 100.00%.The classification performance of the RBEBT is greater than 95%,which can prove that the RBEBT is an effective model to classify brain tumors.

Automated Brain Tumor Diagnosis Using Deep Residual U-Net Segmentation Model

Automated segmentation and classification of biomedical images act as a vital part of the diagnosis of brain tumors(BT).A primary tumor brain analysis suggests a quicker response from treatment that utilizes for improving patient survival rate.The location and classification of BTs from huge medicinal images database,obtained from routine medical tasks with manual processes are a higher cost together in effort and time.An automatic recognition,place,and classifier process was desired and useful.This study introduces anAutomatedDeepResidualU-Net Segmentation with Classification model(ADRU-SCM)for Brain Tumor Diagnosis.The presentedADRUSCM model majorly focuses on the segmentation and classification of BT.To accomplish this,the presented ADRU-SCM model involves wiener filtering(WF)based preprocessing to eradicate the noise that exists in it.In addition,the ADRU-SCM model follows deep residual U-Net segmentation model to determine the affected brain regions.Moreover,VGG-19 model is exploited as a feature extractor.Finally,tunicate swarm optimization(TSO)with gated recurrent unit(GRU)model is applied as a classification model and the TSO algorithm effectually tunes theGRUhyperparameters.The performance validation of the ADRU-SCM model was tested utilizing FigShare dataset and the outcomes pointed out the better performance of the ADRU-SCM approach on recent approaches.

Computer-Aided Diagnosis Model Using Machine Learning for Brain Tumor Detection and Classification

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

Brain Tumor:Hybrid Feature Extraction Based on UNet and 3DCNN

Automated segmentation of brain tumors using Magnetic Resonance Imaging(MRI)data is critical in the analysis and monitoring of disease development.As a result,gliomas are aggressive and diverse tumors that may be split into intra-tumoral groups by using effective and accurate segmentation methods.It is intended to extract characteristics from an image using the Gray Level Co-occurrence(GLC)matrix feature extraction method described in the proposed work.Using 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 tumor.Using 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 estimates.The U-Net and 3D CNN are used together in this study to get better and more accurate estimates of what is going on.Using the dataset,two models were developed and assessed to provide segmentation maps that differed fundamentally in terms of the segmented tumour sub-region.Then,the estimates was made by two separate models that were put together to produce the final prediction.In 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.

Brain Tumor Identification Using Data Augmentation and Transfer Learning Approach

A brain tumor is a lethal neurological disease that affects the average performance of the brain and can be fatal.In India,around 15 million cases are diagnosed yearly.To mitigate the seriousness of the tumor it is essential to diagnose at the beginning.Notwithstanding,the manual evaluation process utilizing Magnetic Resonance Imaging(MRI)causes a few worries,remarkably inefficient and inaccurate brain tumor diagnoses.Similarly,the examination process of brain tumors is intricate as they display high unbalance in nature like shape,size,appearance,and location.Therefore,a precise and expeditious prognosis of brain tumors is essential for implementing the of an implicit treatment.Several computer models adapted to diagnose the tumor,but the accuracy of the model needs to be tested.Considering 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 V3.Data 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 performance.An accuracy of 96.2%is obtained for AlexNet with augmentation and performed better than ResNet 50 and Inception V3 for the 120th epoch.With the suggested model with higher accuracy,it is highly reliable if brain tumors are diagnosed with available datasets.

New advances in radiomics of gastrointestinal stromal tumors

Gastrointestinal stromal tumors(GISTs)are uncommon neoplasms of the gastrointestinal tract with peculiar clinical,genetic,and imaging characteristics.Preoperative knowledge of risk stratification and mutational status is crucial to guide the appropriate patients’treatment.Predicting the clinical behavior and biological aggressiveness of GISTs based on conventional computed tomography(CT)and magnetic resonance imaging(MRI)evaluation is challenging,unless the lesions have already metastasized at the time of diagnosis.Radiomics is emerging as a promising tool for the quantification of lesion heterogeneity on radiological images,extracting additional data that cannot be assessed by visual analysis.Radiomics applications have been explored for the differential diagnosis of GISTs from other gastrointestinal neoplasms,risk stratification and prediction of prognosis after surgical resection,and evaluation of mutational status in GISTs.The published researches on GISTs radiomics have obtained excellent performance of derived radiomics models on CT and MRI.However,lack of standardization and differences in study methodology challenge the application of radiomics in clinical practice.The purpose of this review is to describe the new advances of radiomics applied to CT and MRI for the evaluation of gastrointestinal stromal tumors,discuss the potential clinical applications that may impact patients’management,report limitations of current radiomics studies,and future directions.

Coupling radiomics analysis of CT image with diversification of tumor ecosystem: A new insight to overall survival in stage Ⅰ-Ⅲ colorectal cancer

Objective: This study aimed to establish a method to predict the overall survival(OS) of patients with stage Ⅰ-Ⅲ colorectal cancer(CRC) through coupling radiomics analysis of CT images with the measurement of tumor ecosystem diversification.Methods: We retrospectively identified 161 consecutive patients with stage Ⅰ-Ⅲ CRC who had underwent radical resection as a training cohort. A total of 248 patients were recruited for temporary independent validation as external validation cohort 1, with 103 patients from an external institute as the external validation cohort 2. CT image features to describe tumor spatial heterogeneity leveraging the measurement of diversification of tumor ecosystem, were extracted to build a marker, termed the EcoRad signature. Multivariate Cox regression was used to assess the EcoRad signature, with a prediction model constructed to demonstrate its incremental value to the traditional staging system for OS prediction.Results: The EcoRad signature was significantly associated with OS in the training cohort [hazard ratio(HR)=6.670;95% confidence interval(95% CI): 3.433-12.956;P<0.001), external validation cohort 1(HR=2.866;95% CI: 1.646-4.990;P<0.001) and external validation cohort 2(HR=3.342;95% CI: 1.289-8.663;P=0.002).Incorporating the EcoRad signature into the prediction model presented a higher prediction ability(P<0.001) with respect to the C-index(0.813, 95% CI: 0.804-0.822 in the training cohort;0.758, 95% CI: 0.751-0.765 in the external validation cohort 1;and 0.746, 95% CI: 0.722-0.770 in external validation cohort 2), compared with the reference model that only incorporated tumor, node, metastasis(TNM) system, as well as a better calibration,improved reclassification and superior clinical usefulness.Conclusions: This study establishes a method to measure the spatial heterogeneity of CRC through coupling radiomics analysis with measurement of diversification of the tumor ecosystem, and suggests that this approach could effectively predict OS and could be used as a supplement for risk stratification among stage Ⅰ-Ⅲ CRC patients.

Effectiveness of Deep Learning Models for Brain Tumor Classification and Segmentation

A brain tumor is a mass or growth of abnormal cells in the brain.In children and adults,brain tumor is considered one of the leading causes of death.There are several types of brain tumors,including benign(non-cancerous)and malignant(cancerous)tumors.Diagnosing brain tumors as early as possible is essential,as this can improve the chances of successful treatment and survival.Considering 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 tumors.These pre-trained and integrated deep learning models have been used on the publicly available dataset from The Cancer Genome Atlas.The dataset consists of 120 patients.The pre-trained models have been used to classify tumor or no tumor images,while integrated models are applied to segment the tumor region correctly.We have evaluated their performance in terms of loss,accuracy,intersection over union,Jaccard distance,dice coefficient,and dice coefficient loss.From pre-trained models,the U-Net model achieves higher performance than other models by obtaining 95%accuracy.In contrast,U-Net with ResNet-50 out-performs all other models from integrated pre-trained models and correctly classified and segmented the tumor region.

Salp Swarm Algorithm with Multilevel Thresholding Based Brain Tumor Segmentation Model

Biomedical image processing acts as an essential part of severalmedical applications in supporting computer aided disease diagnosis. MagneticResonance Image (MRI) is a commonly utilized imaging tool used tosave glioma for clinical examination. Biomedical image segmentation plays avital role in healthcare decision making process which also helps to identifythe affected regions in the MRI. Though numerous segmentation models areavailable in the literature, it is still needed to develop effective segmentationmodels for BT. This study develops a salp swarm algorithm with multi-levelthresholding based brain tumor segmentation (SSAMLT-BTS) model. Thepresented SSAMLT-BTS model initially employs bilateral filtering based onnoise removal and skull stripping as a pre-processing phase. In addition,Otsu thresholding approach is applied to segment the biomedical imagesand the optimum threshold values are chosen by the use of SSA. Finally,active contour (AC) technique is used to identify the suspicious regions in themedical image. A comprehensive experimental analysis of the SSAMLT-BTSmodel is performed using benchmark dataset and the outcomes are inspectedin many aspects. The simulation outcomes reported the improved outcomesof the SSAMLT-BTS model over recent approaches with maximum accuracyof 95.95%.

An Effective Diagnosis System for Brain Tumor Detection and Classification

A brain tumor is an excessive development of abnormal and uncontrolled cells in the brain.This growth is considered deadly since it may cause death.The brain controls numerous functions,such as memory,vision,and emotions.Due to the location,size,and shape of these tumors,their detection is a challenging and complex task.Several efforts have been conducted toward improved detection and yielded promising results and outcomes.However,the accuracy should be higher than what has been reached.This paper presents a method to detect brain tumors with high accuracy.The method works using an image segmentation technique and a classifier in MATLAB.The 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 approach.The obtained results reached nearly 99.2%of accuracy.The 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 literature.This evaluation shows that the presented system outperforms other approaches regarding the accuracy,precision,and recall.This research discovered that the developed method is extremely useful in detecting brain tumors,given the high accuracy,precision,and recall results.The proposed system directs us to believe that bringing this kind of technology to physicians diagnosing brain tumors is crucial.

Clinical Knowledge-Based Hybrid Swin Transformer for Brain Tumor Segmentation

Accurate tumor segmentation from brain tissues in Magnetic Resonance Imaging(MRI)imaging is crucial in the pre-surgical planning of brain tumor malignancy.MRI images’heterogeneous intensity and fuzzy boundaries make brain tumor segmentation challenging.Furthermore,recent studies have yet to fully employ MRI sequences’considerable and supplementary information,which offers critical a priori knowledge.This paper proposes a clinical knowledge-based hybrid Swin Transformermultimodal brain tumor segmentation algorithmbased on how experts identify malignancies from MRI images.During the encoder phase,a dual backbone network with a Swin Transformer backbone to capture long dependencies from 3D MR images and a Convolutional Neural Network(CNN)-based backbone to represent local features have been constructed.Instead of directly connecting all the MRI sequences,the proposed method re-organizes them and splits them into two groups based on MRI principles and characteristics:T1 and T1ce,T2 and Flair.These aggregated images are received by the dual-stem Swin Transformer-based encoder branch,and the multimodal sequence-interacted cross-attention module(MScAM)captures the interactive information between two sets of linked modalities in each stage.In the CNN-based encoder branch,a triple down-sampling module(TDsM)has been proposed to balance the performance while downsampling.In the final stage of the encoder,the feature maps acquired from two branches are concatenated as input to the decoder,which is constrained by MScAM outputs.The proposed method has been evaluated on datasets from the MICCAI BraTS2021 Challenge.The results of the experiments demonstrate that the method algorithm can precisely segment brain tumors,especially the portions within tumors.