Automatic Brain Tumor Detection Research Articles

Designing efficient brain tumor classifier using hybrid EfficientNet-faster R-CNN deep learning model

Purpose. Brain tumors have historically posed a significant risk to individuals, potentially leading to fatality. However, in contemporary times, they have emerged as one of the most dangerous health issues affecting both children and adults. The effects of this phenomenon result in the unregulated proliferation of brain cells. Therefore, early detection and precise classification of brain tumor is very necessary and important for saving lives and avoid future complications. Within the field of biological image processing, the utilisation of deep learning algorithms offers a distinct and unparalleled experience. The Convolutional Neural Network (CNN) is a pivotal component in brain tumor categorization applications. However, a notable limitation of CNNs is their extensive execution time, primarily attributed to the substantial number of trainable parameters involved. Also, the automatic detection and classification of brain tumor is still a challenging task due to its variability in shape, size and location. Methods. This study proposes a hybrid model for brain tumor classification that combines faster R-CNN and EfficientNet. The objective is to minimise the time required for classification without compromising accuracy. The data collection from Figshare was employed during the model’s development. Results and Conclusion. The hybrid model proposed in this study demonstrates a notable accuracy of 98.96% during the training phase and 99.2% during the testing phase, surpassing the performance of both the EfficientNet model and the Faster R-CNN model when employed individually. The aforementioned findings were derived through a comparative analysis of the Hybrid model, the EfficientNet model, and the Faster R-CNN model.

Brain Tumor Detection Using Deep Learning Technique

Abstract: One of the most aggressive disorders affecting both children and adults is brain tumors. Brain tumors develop very quickly, and if not treated at the proper time, they decrease the patient's chances of survival. It is crucial to find brain tumors at an early stage. To increase patients’ life expectancy, proper treatment planning and precise diagnostics are most important. Magnetic Resonance Imaging (MRI) is the most effective method for finding brain tumors. Therefore, to find the types of tumors, an automated brain tumor detection system is needed [1]. This work uses Deep Learning (DL) architectures like Convolutional Neural Network (CNN) and EfficientNetB0 for Transfer Learning to detect the brain tumor. This model is used to predict the types of brain tumors

Automatic Brain Tumor Detection and Classification Using UNET and Optimized Support Vector Machine

Automatic Brain Tumor Detection and Classification Using UNET and Optimized Support Vector Machine

Two-headed UNetEfficientNets for parallel execution of segmentation and classification of brain tumors: incorporating postprocessing techniques with connected component labelling.

The purpose of this study is to develop accurate and automated detection and segmentation methods for brain tumors, given their significant fatality rates, with aggressive malignant tumors like Glioblastoma Multiforme (GBM) having a five-year survival rate as low as 5 to 10%. This underscores the urgent need to improve diagnosis and treatment outcomes through innovative approaches in medical imaging and deep learning techniques. In this work, we propose a novel approach utilizing the two-headed UNetEfficientNets model for simultaneous segmentation and classification of brain tumors from Magnetic Resonance Imaging (MRI) images. The model combines the strengths of EfficientNets and a modified two-headed Unet model. We utilized a publicly available dataset consisting of 3064 brain MR images classified into three tumor classes: Meningioma, Glioma, and Pituitary. To enhance the training process, we performed 12 types of data augmentation on the training dataset. We evaluated the methodology using six deep learning models, ranging from UNetEfficientNet-B0 to UNetEfficientNet-B5, optimizing the segmentation and classification heads using binary cross entropy (BCE) loss with Dice and BCE with focal loss, respectively. Post-processing techniques such as connected component labeling (CCL) and ensemble models were applied to improve segmentation outcomes. The proposed UNetEfficientNet-B4 model achieved outstanding results, with an accuracy of 99.4% after postprocessing. Additionally, it obtained high scores for DICE (94.03%), precision (98.67%), and recall (99.00%) after post-processing. The ensemble technique further improved segmentation performance, with a global DICE score of 95.70% and Jaccard index of 91.20%. Our study demonstrates the high efficiency and accuracy of the proposed UNetEfficientNet-B4 model in the automatic and parallel detection and segmentation of brain tumors from MRI images. This approach holds promise for improving diagnosis and treatment planning for patients with brain tumors, potentially leading to better outcomes and prognosis.

A Novel Deep Learning Approach for Brain Tumor Segmentation and Classification

Automated brain tumor detection and segmentation are crucial in medical diagnostics as they give details about functional structures and also potential unusual tissue that is required to define surgical strategies. Segmentation of images is one of the most difficult issues in the area of MRI scan analysis research. However, due to low contrast, automatic tumor segmentation, accuracy issues, and ill-defined boundaries remain difficult. For the prevention of brain tumors, an investigation was developed for implementing a novel framework. To overcome the problem faced by traditional existing techniques, this paper proposed an automatic brain tumor segmentation and detection system using the proposed dense convolution neural network without human intervention, where an intrinsic set of features are extracted to perform the classification task. The main aim of this paper is to segment the tumor portion from the affected MRI using a novel clustering approach. Moreover, a lot of scholars have researched the diagnosis of brain tumors, yet the accuracy-based performance metric in diagnosis results is low. A deep learning framework is proposed to evaluate brain disease more accurately using a Black Widow Optimization driven Dense Convolution Neural Network (BW-DCNN). The various performances are assessed, and the results are compared to those of other existing classifiers and approaches.

An Efficient Segmentation and Classification of Brain Tumor Detection using Deep Learning

In medical diagnostics, the identification of brain tumors is a crucial endeavour that frequently necessitates the careful review of intricate imaging data. With the development of deep learning methods, especially convolutional neural networks (CNNs), automated brain tumor detection has become a viable way to help radiologists diagnose patients accurately and quickly. This study provides an extensive overview of current developments in deep learning approaches for brain tumor identification. The suggested techniques usually start with preprocessing medical images, like CT or MRI scans, and then use CNN architectures to extract features. Different CNN architectures, such as U-Net and conventional CNNs, have been used to extract discriminative characteristics from brain pictures. The performance of brain tumor detection systems has also been improved using transfer learning techniques, which make use of pre-trained models on sizable datasets. These results are especially encouraging when there is a shortage of training data. Additionally, research has been done on using ensemble learning approaches to strengthen the generality and durability of brain tumor detection models. By combining several base models into one prediction, these methods improve overall performance and lower the likelihood of overfitting. Furthermore, the incorporation of sophisticated regularisation methods such batch normalisation and dropout has enhanced the deep learning models' capacity for generalisation in brain tumor identification tasks. Moreover, the use of deep learning models in practical clinical environments demands the resolution of issues of uncertainty estimation and model interpretability

DDFC: deep learning approach for deep feature extraction and classification of brain tumors using magnetic resonance imaging in E-healthcare system

This research explores the use of gated recurrent units (GRUs) for automated brain tumor detection using MRI data. The GRU model captures sequential patterns and considers spatial information within individual MRI images and the temporal evolution of lesion characteristics. The proposed approach improves the accuracy of tumor detection using MRI images. The model’s performance is benchmarked against conventional CNNs and other recurrent architectures. The research addresses interpretability concerns by employing attention mechanisms that highlight salient features contributing to the model’s decisions. The proposed model attention-gated recurrent units (A-GRU) results show promising results, indicating that the proposed model surpasses the state-of-the-art models in terms of accuracy and obtained 99.32% accuracy. Due to the high predictive capability of the proposed model, we recommend it for the effective diagnosis of Brain tumors in the E-healthcare system.

Design and Development of Hypertuned Deep learning Frameworks for Detection and Severity Grading of Brain Tumor using Medical Brain MR images.

Brain tumor is a grave illness causing worldwide fatalities. The current detection methods for brain tumors are manual, invasive, and rely on histopathological analysis. Determining the type of brain tumor after its detection relies on biopsy measures and involves human subjectivity. The use of automated CAD techniques for brain tumor detection and classification can overcome these drawbacks. The paper aims to create two deep learning-based CAD frameworks for automatic detection and severity grading of brain tumors - the first model for brain tumor detection in brain MR images and model 2 for the classification of tumors into three types: Glioma, Meningioma, and Pituitary based on severity grading. The novelty of the research work includes the architectural design of deep learning frameworks for detection and classification of brain tumor using brain MR images. The hyperparameter tuning of the proposed models is done to achieve the optimal parameters that result in maximizing the models' performance and minimizing losses. The proposed CNN models outperform the existing state of the art models in terms of accuracy and complexity of the models. The proposed model developed for detection of brain tumors achieved an accuracy of 98.56% and CNN Model developed for severity grading of brain tumor achieved an accuracy of 92.36% on BraTs dataset. The proposed models have an edge over the existing CNN models in terms of less complexity of the structure and appreciable accuracy with low training and test errors. The proposed CNN Models can be employed for clinical diagnostic purposes to aid the medical fraternity in validating their initial screening for brain tumor detection and its multi-classification.

Brain magnetic resonance images segmentation via improved mixtures of factor analyzers based on dynamic co-clustering

The main goal of this paper is to attain accurate and automatic detection of brain tumors in gray magnetic resonance images using reducing the local dimensions. We propose a novel model called Improved Mixtures of Factor Analyzers based on Dynamic Co-Clustering (IMFADCC) for the detection and localization of brain tumors. After image preprocessing and enhancement, the optimal numbers of row clusters and column clusters corresponding to each row cluster, alongside other model parameters, are determined concurrently based on the size of the tumor by our model. Then, using the optimal values obtained, the image is co-clustered by simultaneously clustering rows and columns until the block containing the tumor is identified. The output image is divided into a certain number of blocks, one of which contains the tumor. For post-processing, the identified block is binarized using minimum error thresholding. The proposed model accurately detects and locates the block binarized within the original image while considering the remainder of the image as background. The effectiveness of the proposed method was assessed by evaluating its accuracy, Dice, intersection on union, geometric mean, receiver operating characteristic curve, specificity, and sensitivity criteria on the BraTS2018, BraTS2019, and BraTS2020 datasets. The results show that our method has significant performance and is highly accurate in detecting tumors of different sizes in complex and high-size images. Moreover, our method outperforms the existing diagnostic methods.

Brain Tumor Detection from MRI Image using Deep Learning

In this project, we propose a deep learning-based approach for the automatic detection of brain tumors from magnetic resonance imaging (MRI) scans. The model utilizes convolutional neural networks (CNNs) to analyze intricate patterns within the images, distinguishing between tumor and non-tumor regions. A diverse dataset, encompassing a range of tumor types and sizes, is employed for robust model training. Through meticulous preprocessing and augmentation, the system enhances its ability to generalize to new and unseen cases. The model is rigorously evaluated using established metrics on a separate test set, demonstrating its efficacy in accurate tumor detection. This research contributes to the advancement of automated medical image analysis, offering a potential tool for timely diagnosis and intervention in neurological healthcare settings.

Automated Brain Tumor Detection and Classification in MRI Images: A Hybrid Image Processing Techniques

Due to the complex structure of brain images, accurately detecting and segmenting brain tumors with Magnetic Resonance Imaging (MRI) is a difficult process. This paper suggests an automated brain tumor identification and segmentation approach employing hybrid salient segmentation with K-Means clustering and hybrid CLEACH-median filter algorithm on MRI images. The proposed method enhances the contrast and detail of MRI images using a hybrid CLEACH-median filter algorithm, and segments the most important features of the images using a hybrid salient segmentation method with K-Means clustering. The proposed method includes a stages classification step to determine the stage of the brain tumor. The findings show that the suggested approach outperformed existing methods in terms of efficiency and accuracy for both detecting and segmenting brain tumors. The suggested technique can be a useful tool for automating the detection and segmentation of brain tumors, which will help radiologists and physicians make quicker and more accurate diagnosis.

YOLOv5x-based Brain Tumor Detection for Healthcare Applications

Brain tumors arise from the emergence of abnormal cells in brain tissue and are considered one of the most perilous conditions affecting individuals of all ages, including both children and adults. The disease advances swiftly, and the likelihood of survival diminishes significantly without prompt and adequate treatment. Hence, accurate diagnosis and meticulous treatment planning play a pivotal role in improving the patient's life expectancy. Neurologists and radiologists play a crucial role in the early detection of brain tumors. However, manually identifying and segmenting brain tumors from Magnetic Resonance Imaging (MRI) data poses significant challenges and is susceptible to inaccuracies. The need for an automated brain tumor detection method becomes imperative to achieve early detection of brain tumors.Objective: The objective of the paper is to measure the capability of You Only Look Once version 5 (YOLOv5x) in brain tumor detection in the early stage so that patients can be treated accordingly.Methods: YOLOv5x is examined; Brats (Brain Tumor Segmentation) image and roboflow dataset has been used. Model performance is evaluated using precision, recall rate, F1 score, and Mean Average Precision (mAP).Results: YOLOv5x exhibited precision (98.7), recall (95.6), F1-score (93), mAP at a learning rate of 0.5 (98.4), and the total time taken for implementation of work is 193.20 minutes.Conclusion: YOLOv5x showed improved performance for the detection of brain tumors on dynamic contrast-enhanced MRI when compared with state of art existing work. It is also the fastest and accurate method indicating a greater potential for clinical application.

Automatic brain tumor detection using adaptive region growing with thresholding methods

<p>Brain cancer affects many people around the world. It's not just limited to the elderly; it is also recognized in children. With the development of image processing, early detection of mental development is possible. Some designers suggest deformable models, histogram averaging, or manual division. Due to constant manual intervention, these cycles can be uncomfortable and tiring. This research introduces a high-level system for the removal of malignant tumors from attractive reverberation images, based on a programmed and rapid distribution strategy for surface extraction and recreation for clinicians. To test the proposed system, acquired tomography images from the Cancer Imaging Archive were used. The results of the study strongly demonstrate that the intended structure is viable in brain tumor detection.</p>

Brain Tumor Detection Using Deep Learning

Brain tumor detection is a critical task in neuroimaging that significantly impacts clinical diagnosis and treatment planning. Conventional methods for tumor detection rely on manual interpretation of medical imaging data, which can be time-consuming and subject to interobserver variability. With the advancements in deep learning, particularly in the realm of convolutional neural networks (CNNs), there has been a growing interest in leveraging these techniques for automated brain tumor detection. This study explores the efficacy of deep learning models in the automated detection and classification of brain tumors from magnetic resonance imaging (MRI) scans. A comprehensive dataset of MRI images representing various tumor types and anatomical regions is employed to train and evaluate the deep learning models. The developed CNN-based architectures are designed to capture intricate patterns and features within the MRI scans, facilitating accurate tumor localization and classification. The research investigates the performance of the deep learning models in distinguishing between healthy brain tissue and different tumor subtypes, including gliomas, meningiomas, and metastatic tumors. Performance metrics such as accuracy, sensitivity, specificity, and area under the curve (AUC) are employed to assess the models' ability to precisely identify tumor regions and provide clinically relevant information to aid healthcare professionals. Furthermore, the study addresses challenges associated with model generalization, interpretability, and scalability for deployment in clinical settings. Strategies for optimizing model robustness, enhancing interpretability, and ensuring seamless integration into existing diagnostic workflows are explored. The findings of this research contribute to the growing body of knowledge regarding the application of deep learning in medical imaging analysis, particularly in the domain of brain tumor detection. The implications of this study extend to the development of more efficient and accurate tools to assist radiologists and clinicians in diagnosing brain tumors, thereby potentially improving patient outcomes and treatment strategies.

A NOVEL DEEP LEARNING METHOD FOR BRAIN TUMOR SEGMENTATION IN MAGNETIC RESONANCE IMAGES BASED ON RESIDUAL UNITS AND MODIFIED U-NET MODEL

Brain tumors are among the most deadly forms of cancer, as the brain is a crucial organ for human activity. Early detection and treatment are key to recovery. An expert’s final decision on tumor diagnosis mainly depends on the evaluation of Magnetic Resonance Imaging (MRI) images. However, the traditional manual assessment process is time-consuming, error-prone, and relies on the experience and knowledge of doctors, along with other unstable factors. An automated brain tumor detection system can assist radiologists and internal medicine experts in detecting and diagnosing brain tumors. This study proposes a novel deep learning model that combines residual units with a modified U-Net framework for brain tumor segmentation tasks in brain MR images. In this study, the U-Net-based framework is implemented with a stack of neural units and residual units and uses Leaky Rectified Linear Unit (LReLU) as the model’s activation function. First, neural units are added before the first layer of downsampling and upsampling to enhance feature propagation and reuse. Then, the stacking of residual blocks is applied to achieve deep semantic information extraction for downsampling and pixel classification for upsampling. Finally, a single-layer convolution outputs the predicted segmented images. The experimental results show that the segmentation Dice Similarity Coefficient of this model is 90.79%, and the model demonstrates better segmentation accuracy than other research models.

Automatic Detection of Brain Tumors Using Genetic Algorithms with Multiple Stages in Magnetic Resonance Images

Biomedicine is still working to solve the problem of detecting brain tumours, one of the biggest problems in the profession today. With improved technology or instrument, early diagnosis of brain cancers is feasible. Classifying brain tumour kinds using patent brain pictures enables automation in automated procedures. Furthermore, the suggested new method is utilised to tell the difference between brain tumours and other brain diseases. To split the tumour and other brain areas, the input picture is first pre-processed. After this, the pictures are divided into different colours and levels, and then they are run through the Gray Level Co-Occurrence and SURF extraction methods to uncover the important details in the photographs. Using genetic optimization, the retrieved characteristics are made smaller. For training and testing tumour classification, the cut-down characteristics are used using an advanced learning technique. The technique's accuracy, error, sensitivity, and specificity are all evaluated alongside the current method. The method has a 90%+ accuracy rate, with less than 2% inaccuracy for all kinds of cancers. Finally, the specificity and sensitivity of every kind are above 90% and 50% correspondingly. Using a genetic algorithm to support the approach is more efficient, since the method it uses has both higher accuracy and specificity than the other techniques.

Automated Brain Tumor Detection using Ideal Shallow Neural Network with Artificial Jellyfish Optimization.

Brain tumors are predicted from Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scan images. In recent years, image processing-based automated tools are developed to predict tumor areas with less human interference. However, such automated tools are suffering from computational complexity and reduced accuracy in certain critical images. In the proposed work, an Ideal Shallow Neural Network (ISNN) is utilized to improve the prediction accuracy, and the computational complexity is reduced by implementing an Artificial Jellyfish Optimization (AJO) algorithm for minimizing the feature dimensionality. The proposed method utilizes MRI images for the verification process as they are more informative than the CT scan image. The BRATS and the Kaggle datasets are used in this work and a Gabor filtering technique is used for noise reduction and a histogram equalization is used for enhancing the tumor boundary regions. The classification results observed from the AJO-ISNN are further forwarded towards the segmentation process and which uses the Centroid Weighted Segmentation (WCS) along with a Grasshopper Optimization Algorithm (GOA) for improving the segmentation over the boundary regions of the brain tumor. The experimental result indicates a classification accuracy of 95.14% on the proposed AJO-ISNN model and AJO-ISNN is comparatively better than the Convolutional Neural Network (CNN) model accuracy of 85.41% and VGG 19 model accuracy of 93.75% while implemented with the AJO optimization model. Similarly, the Dice Similarity Coefficient of the proposed CWS-GOA also reaches 93.15% when performed with both BRATS and Kaggle datasets. Apart from the accuracy attainments the proposed work classifies and segments the tumor region in around 65 seconds on average of 200 image verifications and that is comparatively better than the previous multi-cascaded CNN and the InceptionV3 models.

Automatic Brain Tumor Detection and Volume Estimation in Multimodal MRI Scans via a Symmetry Analysis

In this study, an automated medical decision support system is presented to assist physicians with accurate and immediate brain tumor detection, segmentation, and volume estimation from MRI which is very important in the success of surgical operations and treatment of brain tumor patients. In the proposed approach, first, tumor regions on MR images are labeled by an expert radiologist. Then, an automated medical decision support system is developed to extract brain tumor boundaries and to calculate their volumes by using multimodal MR images. One advantage of this study is that it provides an automated brain tumor detection and volume estimation algorithm that does not require user interactions by determining threshold values adaptively. Another advantage is that, because of the unsupervised approach, the proposed study realized tumor detection, segmentation, and volume estimation without using very large labeled training data. A brain tumor detection and segmentation algorithm is introduced that is based on the fact that the brain consists of two symmetrical hemispheres. Two main analyses, i.e., histogram and symmetry, were performed to automatically estimate tumor volume. The threshold values used for skull stripping were computed adaptively by examining the histogram distances between T1- and T1C-weighted brain MR images. Then, a symmetry analysis between the left and right brain lobes on FLAIR images was performed for whole tumor detection. The experiments were conducted on two brain MRI datasets, i.e., TCIA and BRATS. The experimental results were compared with the labeled expert results, which is known as the gold standard, to demonstrate the efficacy of the presented method. The performance evaluation results achieved accuracy values of 89.7% and 99.0%, and a Dice similarity coefficient value of 93.0% for whole tumor detection, active core detection, and volume estimation, respectively.

Automated Brain Tumor Detection and Segmentation for Treatment Response Assessment Using Amino Acid PET.

Evaluation of metabolic tumor volume (MTV) changes using amino acid PET has become an important tool for response assessment in brain tumor patients. MTV is usually determined by manual or semiautomatic delineation, which is laborious and may be prone to intra- and interobserver variability. The goal of our study was to develop a method for automated MTV segmentation and to evaluate its performance for response assessment in patients with gliomas. Methods: In total, 699 amino acid PET scans using the tracer O-(2-[18F]fluoroethyl)-l-tyrosine (18F-FET) from 555 brain tumor patients at initial diagnosis or during follow-up were retrospectively evaluated (mainly glioma patients, 76%). 18F-FET PET MTVs were segmented semiautomatically by experienced readers. An artificial neural network (no new U-Net) was configured on 476 scans from 399 patients, and the network performance was evaluated on a test dataset including 223 scans from 156 patients. Surface and volumetric Dice similarity coefficients (DSCs) were used to evaluate segmentation quality. Finally, the network was applied to a recently published 18F-FET PET study on response assessment in glioblastoma patients treated with adjuvant temozolomide chemotherapy for a fully automated response assessment in comparison to an experienced physician. Results: In the test dataset, 92% of lesions with increased uptake (n = 189) and 85% of lesions with iso- or hypometabolic uptake (n = 33) were correctly identified (F1 score, 92%). Single lesions with a contiguous uptake had the highest DSC, followed by lesions with heterogeneous, noncontiguous uptake and multifocal lesions (surface DSC: 0.96, 0.93, and 0.81 respectively; volume DSC: 0.83, 0.77, and 0.67, respectively). Change in MTV, as detected by the automated segmentation, was a significant determinant of disease-free and overall survival, in agreement with the physician's assessment. Conclusion: Our deep learning-based 18F-FET PET segmentation allows reliable, robust, and fully automated evaluation of MTV in brain tumor patients and demonstrates clinical value for automated response assessment.

K-mean clustering and local binary pattern techniques for automatic brain tumor detection

Tumors in brains are caused by the unregulated emergence of tissue cells inside the brain. The early diagnosis and determining the precise location of the tumor in magnetic resonance imaging (MRI) and its size are essential for the teams of physicians. Image segmentation is often considered a preliminary step in medical image analyses. K-means clustering has been widely adopted for brain tumor detection. The result of this technique is a list of cluster images. The challenge of this method is the difficulty of selecting the appropriate cluster section that depicts the tumor. In this work, we analyze the influence of different image clusters. Each cluster is then split into the left and right parts. After that, the texture features are depicted in each part. Furthermore, the bilateral symmetry measure is applied to estimate the cluster that contains the tumor. Finally, the connected component labeling is employed to determine the target cluster for brain tumor detection. The developed technique is applied to 30 MRI images. The encouraging accuracy of 87% is obtained.