Automatic Brain Tumor Detection Research Articles

Brain Tumor Detection and Classification on MR Images by a Deep Wavelet Auto-Encoder Model.

The process of diagnosing brain tumors is very complicated for many reasons, including the brain’s synaptic structure, size, and shape. Machine learning techniques are employed to help doctors to detect brain tumor and support their decisions. In recent years, deep learning techniques have made a great achievement in medical image analysis. This paper proposed a deep wavelet autoencoder model named “DWAE model”, employed to divide input data slice as a tumor (abnormal) or no tumor (normal). This article used a high pass filter to show the heterogeneity of the MRI images and their integration with the input images. A high median filter was utilized to merge slices. We improved the output slices’ quality through highlight edges and smoothened input MR brain images. Then, we applied the seed growing method based on 4-connected since the thresholding cluster equal pixels with input MR data. The segmented MR image slices provide two two-layer using the proposed deep wavelet auto-encoder model. We then used 200 hidden units in the first layer and 400 hidden units in the second layer. The softmax layer testing and training are performed for the identification of the MR image normal and abnormal. The contribution of the deep wavelet auto-encoder model is in the analysis of pixel pattern of MR brain image and the ability to detect and classify the tumor with high accuracy, short time, and low loss validation. To train and test the overall performance of the proposed model, we utilized 2500 MR brain images from BRATS2012, BRATS2013, BRATS2014, BRATS2015, 2015 challenge, and ISLES, which consists of normal and abnormal images. The experiments results show that the proposed model achieved an accuracy of 99.3%, loss validation of 0.1, low FPR and FNR values. This result demonstrates that the proposed DWAE model can facilitate the automatic detection of brain tumors.

RADI-12. Deep learning for automatic detection and contouring of metastatic brain tumors in stereotactic radiosurgery: a retrospective analysis with an FDA-cleared software algorithm

IntroductionArtificial intelligence-based tools can significantly impact detection and segmentation of brain metastases for stereotactic radiosurgery (SRS). VBrain is a deep learning algorithm, recently FDA-cleared, to assist in brain tumor contouring. In this study, we aimed to further validate this tool in patients treated with SRS for brain metastases at Stanford Cancer Center.MethodsWe included randomly selected patients with brain metastases treated with SRS from 2008 to 2020. Computed tomography (CT) and axial T1-weighted post-contrast magnetic resonance (MR) image data were extracted for each patient and uploaded to VBrain. Subsequent analyses compared the output contours from VBrain with the physician-defined contours used for SRS. A brain metastasis was considered “detected” when the VBrain “predicted” contours overlapped with the corresponding physician contours (“ground-truth” contours). We evaluated performance against ground-truth contours using the following metrics: lesion-wise Dice similarity coefficient (DSC), lesion-wise average Hausdorff distance (AVD), false positive count (FP), and lesion-wise sensitivity (%).ResultsWe analyzed 60 patients with 321 intact brain metastases treated over 70 SRS courses. Resection cavities were excluded from the analysis. The median (range) tumor size was 132 mm3 (7 to 24,765). Input CT scan slice thickness was 1.250 mm, and median (range) pixel resolution was 0.547 mm (0.457 to 0.977). Input MR scan median (range) slice thickness was 1.000 mm (0.940 to 2.000), and median (range) pixel resolution was 0.469 mm (0.469 to 1.094). In assessing VBrain performance, we found mean lesion-wise DSC to be 0.70, mean lesion-wise AVD to be 9.40% of lesion size (0.805 mm), mean FP to be 0.657 tumors per case, and lesion-wise sensitivity to be 84.5%.ConclusionRetrospective analysis of our brain metastases cohort using a deep learning algorithm yielded promising results. Integration of VBrain into the clinical workflow can provide further clinical and research insights.

Brain Tumor Detection using Enhanced Convolution Neural Network for MR Images

Brain tumor is a mass that grows unevenly in the brain and directly affects human life. The mass occurs spontaneously because of the tissues surrounding the brain or the skull. There are two types of Brain tumor such as Benign and Malignant. Malignant brain tumors contain cancer cells and grow quickly and spread through to other brain and spine regions as well. Accurate and prompt diagnosis of brain tumors is essential for implementing an effective treatment of this disease. Brain images produced by the Magnetic Resonance Imaging (MRI) technique are a rich source of data for brain tumor diagnosis and treatment in the medical field. Due to the existence of a large number of features compared to the other imaging types. The performance of existing methods is inadequate considering the medical significance of the classification problem. Earlier methods relied on manually delineated tumor regions, prior to classification. This prevented them from being fully automated. The automatic algorithms developed using CNN and its variants could not achieve an influential improvement in performance. In order to overcome such an issue, the proposed one is automatic brain tumor detection system, which is “ Enhanced Convolution Neural Network (CNN) Algorithm for MRI Images” for the detection of brain tumor is useful to detect and classify the Glioma part into low Glioma and high Glioma.

Machine Learning Approach for Brain Tumor Detection and Segmentation

Brain tumor is one type of disease that affects the brain directly. MRI is the finest imaging technique for a brain tumor and features information about tumor size, location, and type. MR images are most appropriate for brain studies because it has the best content in soft tissue. The segmentation, detection, and extraction of contaminated tumor area from magnetic resonance (MR) images are prime concerns, but very tedious tasks for radiologists or medical practitioners, and the accuracy depends on their experience. The automatic brain tumor detection and segmentation of MR images help the clinical experts to carry the treatment in a specific direction. The image segmentation methods play a very important role in automatic segmentation of MR images. The research article emphasises the comparative performance analysis of the different image segmentation algorithms such as Otsu's, watershed, level set, k-means, and DWT for brain tumor detection application. The MATLAB simulation is performed for all these algorithms on online images of brain tumor image segmentation benchmark (BRATS) dataset-2012. The performance of these methods is analysed based on response time and measures such as precision, recall, and accuracy. The predicted accuracy of Otsu's, watershed, level set, k-means and DWT algorithms using machine-learning model are 73.90%, 78.12%. 81.90%, 84.75%, and 88.12%, respectively. DWT has proven the good score for tumor detection applications.

Deep Learning Approach in Brain Tumor Classification

Various Computer-Aided Diagnosis (CAD) systems have been recently used in medical imaging to assist radiologist about their patients. Generally, various image technique such as Computer Tomography (CT), Magnetic Resonance Imaging (MRI) and ultrasound image are used to evaluate the tumor in a brain, lung, liver, breast, prostate etc., Especially, in this work MRI images are used to diagnose tumor in the brain. For full assistance of radiologists and better analysis of Magnetic Resonance Imaging, classification of brain tumor is essential procedure. The automatic classification scheme is essential to prevent the death rate of human. Deep learning is the newest and the current trend of machine learning field that paid a lot of the researchers’ attention in the recent few years. As a proven powerful machine learning tool, deep learning was widely used in several applications for solving various complex problems that require extremely high accuracy and sensitivity, particularly in the medical field. Tumor regions from an MR images are segmented using a deep learning technique. The automatic brain tumor classification is very challenging task in large spatial and structural variability of surrounding region of brain tumor. In this work, automatic brain tumor detection using Convolutional Neural Networks (CNN) classification. In general, brain tumor is one of the most common and aggressive malignant tumor diseases which is leading to very short expected life if it is diagnosed at higher grade. The deeper architecture design is performed by using small kernels. Other performance measures used in this study are the accuracy, sensitivity and specificity.

Automatic brain tumour diagnostic method based on a back propagation neural network and an extended set-membership filter

BackgroundDiagnosing brain tumours remains a challenging task in clinical practice. Despite their questionable accuracy, magnetic resonance image (MRI) scans are presently considered the optimal facility for assessing the growth of tumours. However, the efficiency of manual diagnosis is low, and high computational cost and poor convergence restrict the application of machine learning methods. This study aims to design a method that can reliably diagnose brain tumours from MRI scans. MethodsFirst, image pre-processing (which includes background removal, size standardization, noise removal, and contrast enhancement) is utilized to normalize the images. Then, grey level co-occurrence matrix features are selected as texture features of the brain MRI scans. Finally, a method combining a back propagation neural network (BPNN) and an extended set-membership filter (ESMF) is proposed to classify features and perform image classification. ResultsA total of 304 patient MRI series (247 images of brains with tumours and 57 images of normal brains) were included and assessed in this study. The results revealed that our proposed method can achieve an accuracy of 95.40% and has classification accuracies of 97.14% and 88.24% for brain tumour and normal brain, respectively. ConclusionThis study proposes an automatic brain tumour detection model constructed using a combination of BPNN and ESMF. The model is found to be able to accurately classify brain MRI scans as normal or tumour images.

Brain tumor detection in MR image using superpixels, principal component analysis and template based K-means clustering algorithm

In the present era, human brain tumor is the extremist dangerous and devil to the human being that leads to certain death. Furthermore, the brain tumor arises more complexity of patients life with time. As a result, early detection of tumors is most crucial to save and prolong the patient’s lifetime. Therefore, enhanced brain tumor detection is required in medical fields. Automatic human brain tumor detection in magnetic resonance imaging (MRI) is playing a vital role in several symptomatic and cures applications. However, the existing schemes (e.g., random forest, Fuzzy C-means, artificial neural network (ANN) and wavelet transform) can detect brain tumors with insufficient accuracy and longer execution time (in minutes). In this paper, we propose an enhanced brain tumor detection scheme based on the template-based K-means (TK) algorithm with superpixels and principal component analysis (PCA) which efficiently detects the human brain tumors in lower execution time. At first, we extract essential features using both superpixels and PCA which helps accurately to detect brain tumors. Then, image enhancement is done using a filter that helps to improve accuracy. Finally, the image segmentation is performed through TK-means clustering algorithm to detect the brain tumor. The experimental results show that the proposed detection scheme achieves a better accuracy and a reduced execution time (in seconds) than other existing schemes for the detection of brain tumor in MR image.

A survey of the identification strategies of brain tumors for MR images

A substantial rise in medical cases associated with brain tumor has been seen in recent years, making it the 10th most common type of tumor Impacting both children and adults. Due to the rising refining of medical picture technologies, Brain Tumor [BT] and their study are of considerable concern Medical image processing concepts have been used successfully in diagnosis of Tumor. For its non-invasive imaging properties, science is more oriented toward MR. Diagnosis or identification mechanisms assisted by computers have become problematic and are still an Open concern due to heterogeneity in tumor shapes, locations, and sizes. Many experts in medical field have carried out notable study work on automated detection of tumor strategies based on segmentation, grouping and variations of automatic brain tumor detection. Different brain tumor identification methods for MR images are analysed in the manuscript, including the assets and challenges found with all techniques to detect different forms of BT. The survey presented here is aimed at supporting the researchers identify the important features of types of brain tumor and identify different segmentation/classification approaches that are effective in identifying a variety of tumor types of disorders of the brain. The manuscript covers the most important approaches, procedures and operating practices. Brain tumor identification rules, priorities, restrictions, and their potential snags on MR picture.

Automated brain tumor detection and classification using weighted fuzzy clustering algorithm, deep auto encoder with barnacle mating algorithm and random forest classifier techniques

AbstractMagnetic resonance imaging (MRI) scan analysis is an effective tool that accurately detects abnormal brain tissue. This manuscript proposes the strategy of segmentation of brain tumors in MRI images and uses the technique of weighted fuzzy factor based on kernel metrics. Here, a deep auto encoder (DAE) with barnacle mating algorithm (BMOA) and random forest (RF) classifier are used to tumor stage classification to enhance the accuracy of prediction. This manuscript presents a deep‐neural network structure, integrating DAE and RF, with a classification unit, which is used for the classification of brain MRI. Finally, the segmented features are graded by the DAE with BMOA and RF. The proposed method is executed in MATLAB site and the performance is analyzed with existing methods. The experimental outcomes of the proposed method are assessed and validated in MR brain images depending on accuracy, sensitivity, and specificity for performance with quality analysis.

A novel hybrid approach to detect brain tumor in MRI images

Image segmentation is the fundamental step in medical image analysis. Segmentation is a procedure to separate similar portions of images showing resemblance in different features such as color, intensity, or texture. Grayscale images are mostly used for the segmentation of medical images. Tumors are commonly stated as the abnormal growth of tissues and the brain tumor is a diseased part in the body tissues that is an abnormal mass in which the growth rate of cells is irrepressible. The mortality rate of people has raised over the past years due to brain tumors, hence this area has gained the attention of researchers. Automatic detection of brain tumors is a challenging task because it involves pathology, functional physics of MRI along with intensity and shapes analysis of MR image. After all, tumor shape, size, location, and intensity vary for each infected case. In this work, a novel hybrid approach is implemented by combing watershed segmentation, level set segmentation and K means clustering. First, the image is preprocessed by removing the skull. Watershed segmentation is applied to this preprocessed image. Level set segmentation is applied to the previous step. Finally, k means clustering is applied as the final step to detect tumor parts accurately. This Hybrid approach is compared with other four techniques such as Threshold segmentation, K means clustering, Watershed segmentation, and Level set-based segmentation methods. Statistical and Visual analysis is performed. It is found that the hybrid approach has better specificity, accuracy, and precision among all four techniques. Further, it is able to detect tumors more accurately. This research could help clinicians in surgical planning, treatment planning and accurately segmenting the tumor part with the most accurate method.

Automatic and accurate abnormality detection from brain MR images using a novel hybrid UnetResNext-50 deep CNN model

The automatic and accurate detection and segmentation of brain tumors is a very tedious and challenging task for medical experts and radiologists. This paper proposes a hybrid deep convolutional neural network (CNN) model with a large number of layers and parameters for the automatic and accurate prediction and segmentation of brain tumors from the magnetic resonance imaging (MRI) images. The proposed model has a skip connection with cardinality which solves the problem of gradient degradation and also reduces the computational cost of Deep CNN architecture and it also improves the pixel quality at the decoder side. MRI dataset containing a total of 3929 MR images including 1373 images with tumors and 2556 images of normal type (without tumor). The dataset is preprocessed and augmented with 21 parameters before feeding the train images to the proposed models for learning. Performance metrics used for the evaluation of model efficiency are Jaccard Index, DICE score, F1-score, accuracy, precision, and recall. Our model performance is also evaluated by comparing it with the other two models UnetResNet-50 and Vanilla Unet and also with state-of-art techniques. In the post-processing stage, the scores of segmented tumor areas are calculated based on the scores of IoU and DICE and are also presented for comparison with the original images. Performance evaluation metrics show that the proposed model UnetResNext-50 shows excellent efficacy with 99.7% accuracy and a 95.73% DICE score.

Integration of optimized neural network and convolutional neural network for automated brain tumor detection

Purpose The purpose of this paper is to develop an efficient brain tumor detection model using the beneficial concept of hybrid classification using magnetic resonance imaging (MRI) images Brain tumors are the most familiar and destructive disease, resulting to a very short life expectancy in their highest grade. The knowledge and the sudden progression in the area of brain imaging technologies have perpetually ready for an essential role in evaluating and concentrating the novel perceptions of brain anatomy and operations. The system of image processing has prevalent usage in the part of medical science for enhancing the early diagnosis and treatment phases. Design/methodology/approach The proposed detection model involves five main phases, namely, image pre-processing, tumor segmentation, feature extraction, third-level discrete wavelet transform (DWT) extraction and detection. Initially, the input MRI image is subjected to pre-processing using different steps called image scaling, entropy-based trilateral filtering and skull stripping. Image scaling is used to resize the image, entropy-based trilateral filtering extends to eradicate the noise from the digital image. Moreover, skull stripping is done by Otsu thresholding. Next to the pre-processing, tumor segmentation is performed by the fuzzy centroid-based region growing algorithm. Once the tumor is segmented from the input MRI image, feature extraction is done, which focuses on the first-order and higher-order statistical measures. In the detection side, a hybrid classifier with the merging of neural network (NN) and convolutional neural network (CNN) is adopted. Here, NN takes the first-order and higher-order statistical measures as input, whereas CNN takes the third level DWT image as input. As an improvement, the number of hidden neurons of both NN and CNN is optimized by a novel meta-heuristic algorithm called Crossover Operated Rooster-based Chicken Swarm Optimization (COR-CSO). The AND operation of outcomes obtained from both optimized NN and CNN categorizes the input image into two classes such as normal and abnormal. Finally, a valuable performance evaluation will prove that the performance of the proposed model is quite good over the entire existing model. Findings From the experimental results, the accuracy of the suggested COR-CSO-NN + CNN was seemed to be 18% superior to support vector machine, 11.3% superior to NN, 22.9% superior to deep belief network, 15.6% superior to CNN and 13.4% superior to NN + CNN, 11.3% superior to particle swarm optimization-NN + CNN, 9.2% superior to grey wolf optimization-NN + CNN, 5.3% superior to whale optimization algorithm-NN + CNN and 3.5% superior to CSO-NN + CNN. Finally, it was concluded that the suggested model is superior in detecting brain tumors effectively using MRI images. Originality/value This paper adopts the latest optimization algorithm called COR-CSO to detect brain tumors using NN and CNN. This is the first study that uses COR-CSO-based optimization for accurate brain tumor detection.

Automatic brain tumour detection using image processing and data mining techniques

Automatic brain tumour detection using image processing and data mining techniques

Automatic brain tumour detection using image processing and data mining techniques

In recent days, extensive analysis on magnetic resonance imaging (MRI) is being performed to understand the complex structure of human brain. Generally, pathological regions in the brain are identified through various MRI acquisition techniques. Depending upon the MRI technique specific regions may be exhibited distinctly than the other brain regions. These images are analysed computationally to identify the abnormal regions. In this work, high grade glioma images are utilised to detect the tumour regions in the brain using image processing and data mining techniques. Broadly, the pixels are grouped into tumour and non-tumour pixels using unsupervised as well as supervised data mining methods. Further, the tumour pixels are classified into four classes namely, oedema, necrosis, enhancing tumour and non-enhancing tumour using supervised classification methods. K-means clustering could detect the pixel clusters with an accuracy of 94.64% whereas random forest classifier could identify the pixel classes 99.50% correctly. Random forest could achieve better results in multi-label classification of the tumour when compared to other classifiers.

Brain Tumor Detection Using Deep Learning Techniques

Brain tumor is considered as one of the aggressive diseases, among children and adults. Brain tumors grow very fast and if not treated well, the survival chances of the patient are very less. Early detection of brain tumors is very important. Proper treatment planning and accurate diagnostics is at the topmost priority to improve life expectancy of the patients. The best technique to detect brain tumor is Magnetic Resonance Imaging. The MRI images are examined by the radiologist. Manual examination can be error prone due to the level of complexities involved in brain tumors and their properties. Hence an automated brain tumor detection system is required to detect tumors at its early stage. This paper uses deep learning based Depth wise separable Convolution Neural Network to detect the tumor based on the MRI images. Experiments are done on the public dataset available at kaggle. Experimental Results have shown that Depth wise Separable Convolution Neural Network gives better accuracy as compared Support Vector Machine, K Nearest Neighbour and Convolution Neural Network.

Deep Learning-aided Brain Tumor Detection: An Initial ‎Experience based Cloud Framework ‎

Lately, the uncertainty of diagnosing diseases increased and spread due to the huge intertwined and ambiguity of symptoms, that leads to overwhelming and hindering the reliability of the diagnosis ‎process. Since tumor detection from ‎MRI scans depends mainly on the specialist experience, ‎misdetection will result an inaccurate curing that might cause ‎critical harm consequent results. In this paper, detection service for brain tumors is introduced as ‎an aiding function for both patients and specialist. The ‎paper focuses on automatic MRI brain tumor detection under a cloud based framework for multi-medical diagnosed services. The proposed CNN-aided deep architecture contains two phases: the features extraction phase followed by a detection phase. The contour ‎detection and binary segmentation were applied to extract the region ‎of interest and reduce the unnecessary information before injecting the data into the model for training. The brain tumor ‎data was obtained from Kaggle datasets, it contains 2062 cases, ‎‎1083 tumorous and 979 non-tumorous after preprocessing and ‎augmentation phases. The training and validation phases have been ‎done using different images’ sizes varied between (16, 16) to ‎‎ (128,128). The experimental results show 97.3% for detection ‎accuracy, 96.9% for Sensitivity, and 96.1% specificity. Moreover, ‎using small filters with such type of images ensures better and faster ‎performance with more deep learning.‎

Deep Learning-aided Brain Tumor Detection: An Initial ‎Experience based Cloud Framework ‎

Lately, the uncertainty of diagnosing diseases increased and spread due to the huge intertwined and ambiguity of symptoms, that leads to overwhelming and hindering the reliability of the diagnosis ‎process. Since tumor detection from ‎MRI scans depends mainly on the specialist experience, ‎misdetection will result an inaccurate curing that might cause ‎critical harm consequent results. In this paper, detection service for brain tumors is introduced as ‎an aiding function for both patients and specialist. The ‎paper focuses on automatic MRI brain tumor detection under a cloud based framework for multi-medical diagnosed services. The proposed CNN-aided deep architecture contains two phases: the features extraction phase followed by a detection phase. The contour ‎detection and binary segmentation were applied to extract the region ‎of interest and reduce the unnecessary information before injecting the data into the model for training. The brain tumor ‎data was obtained from Kaggle datasets, it contains 2062 cases, ‎‎1083 tumorous and 979 non-tumorous after preprocessing and ‎augmentation phases. The training and validation phases have been ‎done using different images’ sizes varied between (16, 16) to ‎‎ (128,128). The experimental results show 97.3% for detection ‎accuracy, 96.9% for Sensitivity, and 96.1% specificity. Moreover, ‎using small filters with such type of images ensures better and faster ‎performance with more deep learning.‎

Brain Tumor Image De-noising Using Edge Adaptive Total Variation Denoising Algorithm

Brain tumor is an abnormality in brain cells caused due to mutations in brain cells. Detection of these tumors is done by using Magnetic Resonance Imaging (MRI) scanning. Researchers have been working on the automated brain tumor detection and classification techniques to assist doctors in diagnosis process. The MRI scans obtained are sometimes affected by noise. To eliminate this noise, image denoising techniques are used. But these techniques remove the noise at the cost of blurring the edges by lowering the resolution and the quality of the image. Retaining the edges present in the brain tumor image is very important for further processing. This paper presents a brain tumor denoising technique by using Edge adaptive total variation. The proposed algorithm analyses the edges present at every pixel while denoising, by using the gradient angle at the pixel. This enhances the performance of the algorithm by retaining the edges of the image while denoising. The algorithm has been compared with existing techniques and has proven to be very effective in removing noise from the image.

Brain Tumor Diagnosis Support System: A Decision Fusion Framework

The early and accurate detection of brain tumors is important in providing effective and efficient therapy and thus can result in increased survival rates. Current image-based tumor detection and diagnosis methods depend heavily on the interpretation of the neuro specialists and/or radiologists. Therefore, it is quite possible for the interpretation process to be time-consuming, and prone to human error and subjectivity. Automatic detection and classification of brain tumors have the potential to achieve efficiency and higher degree of predictable accuracy. However, it is well established that the accuracy performance of automatic detection and classification techniques varies from technique to technique, and tends to be image modality dependent. Thus, it is prudent to explore the variability in the performance of these techniques as a means to achieve consistent high accuracy performance. This paper presents a framework for fusing multiple tumor classifiers. The fusion process is based on the Dempster Shafer evidence fusion theory. Several tumor classifiers are employed. Experimental results will be presented to validate the efficiency of the proposed framework. It is concluded that fusing the classification decisions made by the various classifiers it is conceivable that efficient and consistent high accuracy classification performance can be attained.

A study of classification and feature extraction techniques for brain tumor detection

Medical imaging aids in the analysis of interior parts of the human body such as the functioning of the organs or tissues for early treatment of diseases. Many different types of medical imaging technologies exist, for example, X-ray radiography, magnetic resonance imaging, endoscopy, positron emission tomography, CT scan (computed tomography), and many more. A tumor is an abnormal tissue in the brain which causes damage to the functioning of the cell. Therefore, brain tumor detection is an incredibly tricky task. Manual detection of a tumor is quite risky as it involves the insertion of a needle in the brain. Thus, there is a need for automated brain tumor detection systems. The well-timed detection of the tumor can add to accurate treatment and can increase the survival rate of patients. From machine learning techniques, namely K-nearest neighbor, support vector machine, and more to soft computing techniques, namely artificial neural network, self-organizing map, and others hold a significant stand in detection and categorization of brain tumor. Various methods including deep learning-based classifiers such as convolutional neural network, recurrent neural network, deep belief network (DBN), and others are used to make it easier to detect the tumor. Hybrid classifiers were also used for classification systems such as combining the machine learning approach with soft computing. This study is to summarize and compare the work of various authors on automatic brain tumor detection using medical imaging. Based on the accuracy, specificity, and sensitivity parameters, the results of different techniques are analyzed and compared graphically.