Early Detection Of Brain Tumors Research Articles

Brain tumor magnetic resonance image classification: a deep learning approach

From the past decade, many researchers are focused on the brain tumor detection mechanism using magnetic resonance images. The traditional approaches follow the feature extraction process from bottom layer in the network. This scenario is not suitable to the medical images. To address this issue, the proposed model employed Inception-v3 convolution neural network model which is a deep learning mechanism. This model extracts the multi-level features and classifies them to find the early detection of brain tumor. The proposed model uses the deep learning approach and hyper parameters. These parameters are optimized using the Adam Optimizer and loss function. The loss function helps the machines to model the algorithm with input data. The softmax classifier is used in the proposed model to classify the images in to multiple classes. It is observed that the accuracy of the Inception-v3 algorithm is recorded as 99.34% in training data and 89% accuracy at validation data.

Visual Disturbances as an Early Important Sign of Brain Tumor: A Case Report

Purpose: to raise attention for careful evaluation of ocular signs and symptoms, especially mild visual disturbances, for early detection of brain tumors. Case Presentation: A 45-year-old woman presented with a one-month history of gradual vision loss in both eyes. There was also a history of headache for over a year before the vision loss symptom appeared. There was no history of trauma, ocular pain, eye redness, or lacrimation. The visual acuity (VA) of both the right and left eyes with the best-corrected visual acuity (BCVA) was 6/30. Confrontation visual field test of both eyes revealed unremarkable results upon examination. The intraocular pressures (IOP) of both eyes were 11 mm Hg. Anterior segment examination of both eyes was within normal limit except for the direct light reflexes were decreased. Relative afferent pupillary defect (RAPD) of both eyes was difficult to assess. Fundoscopy examination of both eyes revealed papilledema. The retinal fields and macula were normal bilaterally. Computerized tomography (CT) scan demonstrated a hyperdense and calcified lesion in the right frontal lobe. The dimensions of the lesion were measured as 69.9 x 52.1 mm. After contrast, the CT scan showed a strongly enhanced lesion with thickening of the right frontal bone. Both of the anterior horns of the lateral ventricle were pressed by the lesion. There was a midline shift to the left by approximately 14 mm. Both of the eyes were within normal limits. Conclusion: Blurred vision and other visual disturbances are common early symptoms in brain tumors, but diagnosis is not made till very late due to late presentation. Early detection of a brain tumor through ocular signs and symptoms and complete neuro-ophthalmic evaluation can help in early diagnosis and appropriate management of brain tumors.

A Review on Comparative analysis and methods of Early detection of Brain tumor using Deep Learning CNN

According to the last Report of International Association of Cancer Registries which is a Global cancer observatory of World health organization has reported 28,000 cases in India each year and more than 24,000 people reportedly die due to brain tumor annually. Harmful Brain tumor have three types that is Glioma, Meningioma and Pituitary tumor. For image data of brain tumor, MRI and CT Scan are mostly used. But as per research study, MRI is used more compare to CT scan and others. Deep learning has a capability to work on very deep neural network process. Convolutional neural network works on more efficient way to improve object detection. The Aim of this paper is to compare different pretrained convolution neural network based developed methods and models to predict or detect tumour symptoms early through Image data using Deep Learning technique. The main focus of this paper about study and review of different CNN models, algorithms, datasets and compared their results for accuracy as well as pictorial view of the tumor shape.

Brain Tumor Segmentation using Multi-View Attention based Ensemble Network

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

Use of stereotactic radiotherapy in treatment of cerebral gliomas

The article considers statistical data on morbidity for primary tumors of the central nervous system, in particular cerebral gliomas. Significance of the problem requires a search for new ways of diagnosis and treatment of cerebral gliomas, and the article summarizes Russian and international literature on diagnosis and treatment of glial tumors. Early detection of brain tumors, appropriate molecular and genetic diagnostics and use of the latest treatment methods such as stereotactic radiotherapy, allow to achieve better long-term results, improve quality of life and lifespan of the patients.

Chaotic Harris Hawks Optimization with Quasi-Reflection-Based Learning: An Application to Enhance CNN Design.

The research presented in this manuscript proposes a novel Harris Hawks optimization algorithm with practical application for evolving convolutional neural network architecture to classify various grades of brain tumor using magnetic resonance imaging. The proposed improved Harris Hawks optimization method, which belongs to the group of swarm intelligence metaheuristics, further improves the exploration and exploitation abilities of the basic algorithm by incorporating a chaotic population initialization and local search, along with a replacement strategy based on the quasi-reflection-based learning procedure. The proposed method was first evaluated on 10 recent CEC2019 benchmarks and the achieved results are compared with the ones generated by the basic algorithm, as well as with results of other state-of-the-art approaches that were tested under the same experimental conditions. In subsequent empirical research, the proposed method was adapted and applied for a practical challenge of convolutional neural network design. The evolved network structures were validated against two datasets that contain images of a healthy brain and brain with tumors. The first dataset comprises well-known IXI and cancer imagining archive images, while the second dataset consists of axial T1-weighted brain tumor images, as proposed in one recently published study in the Q1 journal. After performing data augmentation, the first dataset encompasses 8.000 healthy and 8.000 brain tumor images with grades I, II, III, and IV and the second dataset includes 4.908 images with Glioma, Meningioma, and Pituitary, with 1.636 images belonging to each tumor class. The swarm intelligence-driven convolutional neural network approach was evaluated and compared to other, similar methods and achieved a superior performance. The obtained accuracy was over 95% in all conducted experiments. Based on the established results, it is reasonable to conclude that the proposed approach could be used to develop networks that can assist doctors in diagnostics and help in the early detection of brain tumors.

Brain Tumor Detection and Classification by Hybrid CNN-DWA Model Using MR Images.

Medical image processing is an exciting research area. In this paper, we proposed new brain tumor detection and classification model using MR brain images to help the doctors in early detection and classification of the brain tumor with high performance and best accuracy. The model was trained and validated using five databases, including BRATS2012, BRATS2013, BRATS2014, BRATS2015, and ISLES-SISS 2015. The advantage of the hybrid model proposed is its novelty that is used for the first time; our new method is based on a hybrid deep convolution neural network and deep watershed auto-encoder (CNN-DWA) model. The method consists of six phases, first phase is input MR images, second phase is preprocessing using filter and morphology operation, third phase is matrix that represents MR brain images, fourth is applying the hybrid CNN-DWA, fifth is brain tumor classification, and detection, while sixth phase is the performance of the model using five values. The novelty of our hybrid CNN-DWA model showed the best results and high performance with accuracy around 98% and loss validation 0, 1. Hybrid model can classify and detect the tumor clearly using MR images; comparing with other models like CNN, DNN, and DWA, we discover that the proposed model performs better than the above-mentioned models. Depending on the better performance of the proposed hybrid model, this helps in developing computer-aided system for early detection of brain tumors and helps the doctors to diagnose the patients better.

Design and Development of 3D Brain MRI System Using Deep Neural Networks

A Brain tumor is otherwise known as intracranial tumor. It is a formation of abnormal cells within the brain. A tumor cells grows continuously in the brain and destroys the cells in that specific region causing brain damage. The main problem in the tumor detection is that some normal brain cells tend to behave as tumor cell which leads to misclassification or unwanted brain surgery. A great challenge for the researchers is to identify the region and appropriate tumor mass. Due to this main reason, automated classifications are acquired for the early detection of brain tumor. In this research work, two standard datasets were used to test the developed classification algorithms. In this study, four different deep learning models were utilized to identify the accurate fit model to classify the brain tumor. From the results, it was observed that googlenet has achieved maximum mean classification accuracy of 98.2%, sensitivity 98.67% and specificity 96.17%. Our proposed model can be used to classify the brain tumor more accurately and effectively.

Abstract 2599: Does tumor volume effect the spectroscopic classification of brain cancer patients

Abstract This study focuses on investigating the link between brain tumor volume and the spectroscopic classification between patients with known gliomas and healthy controls. Discrimination of brain cancer vs. non-cancer patients using serum-based ATR-FTIR diagnostics was first developed by Hands et al. achieving sensitivity and specificity values of 92.8% and 91.5% respectively. Cameron et al. then went on to stratifying between specific brain tumor types and was successful in providing a sensitivity of 90.1% and a specificity of 86.3%. Expanding on these studies, it is vital to determine if the size of a tumor has a direct effect on the sensitivity and specificity and whether or not it was only the larger tumors that were being identified as cancerous. A cohort of 90 patients whose tumor volumes were calculated using their MRI images (either T1-weighted contrast enhanced, T2-weighted or FLAIR images), including patients with high-grade glioblastoma multiforme (GBM), and low-grade gliomas such as anaplastic astrocytoma, astrocytoma, oligoastrocytoma and oligodendroglioma, were used for investigation. Utilizing ATR-FTIR spectroscopy coupled with machine learning algorithms these tumor patients were stratified against 87 healthy controls and were classified as either cancer or non-cancer. From these initial findings' sensitivities, specificities and balanced accuracies were greater than 88% and cancer patients with tumor volumes as small as 0.2 cubic cm were correctly identified, demonstrating that classifications are not affected by tumor volume. Both small and low-grade gliomas were identified which shows great promise for this technique to be used as a screening tool or in diagnostics for early detection of brain tumors. Citation Format: Ashton G. Theakstone, Paul M. Brennan, Matthew J. Baker. Does tumor volume effect the spectroscopic classification of brain cancer patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2599.

Automated detection of brain tumor disease using empirical wavelet transform based LBP variants and ant-lion optimization

Early detection of brain tumor is a challenging task that assists medical practitioners in disease diagnosis. This article presents a computer-assisted brain tumor detection scheme using empirical wavelet transform based local binary pattern variant features and ant-lion optimization. Firstly, the input magnetic resonance (MR) image is enhanced using brightness preserving dynamic fuzzy histogram equalization (DFHE) algorithm. Then, significant modes from the input brain MR image are extracted using empirical wavelet transform (EWT), and five different local binary variants are extracted from each EWT mode. Further, the ant-lion feature selection algorithm is employed to select discriminating and relevant features and remove redundant descriptors that lower feature dimensionality. Finally, a support vector machine (SVM) classifier is used to detect the input MR image as healthy or tumorous. The proposed algorithm performance is validated on two widely and publicly available databases using different statistical measures including sensitivity, specificity, precision, F1-measure, and accuracy. A substantial set of experiments are carried out to evaluate the effect of individual descriptors, descriptor fusion, number of EWT decomposition levels, and DFHE enhancement on the classification accuracy. The simulation results obtained confirm that the presented approach performance is better as compared to other existing methods in terms of detection rate.

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.

Brain tumor diagnosis based on metaheuristics and deep learning

AbstractThe high mortality rate associated with brain tumors requires early detection in the early stages to treat and reduce mortality. Due to the complexity of brain tissue, manual diagnosis of the brain and tumor tissues is very time‐consuming and operator dependent. Furthermore, there is a need for experts who can review the images to detect these effects, rendering traditional methods inefficient in their presence. Therefore, the use of automated procedures for the careful examination of tumors can prove useful. In this study, a new metaheuristic‐based system is presented for the early detection of brain tumors. The proposed method implements three main steps, namely tumor segmentation, feature extraction, and classification based on a deep belief network. An improved version of the seagull optimization algorithm is adopted for optimal selection of the features and classification of the images. The simulation results of the proposed method are compared with a few existing methods. The final results demonstrate that the proposed method exhibits superior performance in terms of the CDR, FAR, and FRR indices compared with the other methods.

Review of Automated Computerized Methods for Brain Tumor Segmentation and Classification.

Recently, medical imaging and machine learning gained significant attention in the early detection of brain tumor. Compound structure and tumor variations, such as change of size, make brain tumor segmentation and classification a challenging task. In this review, we survey existing work on brain tumor, their stages, survival rate of patients after each stage, and computerized diagnosis methods. We discuss existing image processing techniques with a special focus on preprocessing techniques and their importance for tumor enhancement, tumor segmentation, feature extraction and features reduction techniques. We also provide the corresponding mathematical modeling, classification, performance matrices, and finally important datasets. Last but not least, a detailed analysis of existing techniques is provided which is followed by future directions in this domain.

Advance computer analysis of magnetic resonance imaging (MRI) for early brain tumor detection

Purpose The brain tumor grows inside the skull and interposes with regular brain functioning. The tumor growth may possibly result in cancer at a later stage. The early detection of brain tumor is crucial for successful treatment of fatal disease. The tumor presence is normally detected by Computed Tomography (CT) or Magnetic Resonance Imaging (MRI) images. The MRI/CT images are highly complex and involve huge data. This requires highly tedious and time-consuming process for detection of small tumors for the neurologists. Thus, there is a need to develop an effective and less time-consuming imaging technique for early detection of brain tumors. Materials and Methods This paper mainly focuses on early detecting and localizing the brain tumor region using segmentation of patient's MRI images. The Matlab software experiments are performed on a set of fifteen tumorous MRI images. In the proposed work, four image segmentation modalities namely watershed transform, k-means clustering, thresholding and Fuzzy C Means Clustering techniques with median filtering have been implemented. Results The results are verified by quantitative comparison of results in terms of image quality evaluation parameters-Entropy, standard deviation and Naturalness Image Quality Evaluator. A remarkable rise in the entropy and standard deviation values has been noticed. Conclusions The watershed transform segmentation with median filtering yields the best quality brain tumor images. The noteworthy improvement in visibility of the MRI images may highly increase the possibilities of early detection and successful treatment of brain tumor disease and thereby assists the clinicians to decide the precise therapies.

Detection and Classification of Brain Tumor Based on Multilevel Segmentation with Convolutional Neural Network

Magnetic Resonance Imaging (MRI) is an important diagnostic technique for early detection of brain Tumor and the classification of brain Tumor from MRI image is a challenging research work because of its different shapes, location and image intensities. For successful classification, the segmentation method is required to separate Tumor. Then important features are extracted from the segmented Tumor that is used to classify the Tumor. In this work, an efficient multilevel segmentation method is developed combining optimal thresholding and watershed segmentation technique followed by a morphological operation to separate the Tumor. Convolutional Neural Network (CNN) is then applied for feature extraction and finally, the Kernel Support Vector Machine (KSVM) is utilized for resultant classification that is justified by our experimental evaluation. Experimental results show that the proposed method effectively detect and classify the Tumor as cancerous or non-cancerous with promising accuracy.

Deep Learning Technique for Brain Tumor Detection using Medical Image Fusion

Brain Tumor detection using Medical image fusion plays an important role in medical field .Using Fusion technique, The medical image can be enhanced to detect the tumor. It is a mechanism of combining various images of same scene into a single fused image to reduce uncertainty and redundancy, also extracting vital information from the source images. The applications used here to detect Brain Tumor are DBN and CNN techniques. This paper emerges a new process of fusing the images to produce efficient and reliable result for detecting the cancerous tissue and early detection of Brain Tumor.

Brain Tumor Analysis in Basf Framework

The medical image processing is extensively used in several areas. In earlier detection and treatment of these diseases is very helpful to find out the abnormality issues in that image. Here there are number of methods available for diagnosis to detect the brain tumor of MRI image. This study deals with there are two main contributions are implemented in this filter method. (1)The extension of adaptive bilateral method to apply sub-bands of low frequency signal decomposed using wavelet transform. A wavelet threshold is combined with adaptive bilateral method to form an innovative structure in image de-noising method. It’s very efficient to eliminate noise in original noisy images. (2) First detected block boundary and texture regions discontinuities to adapt or control the parameters of spatial and intensity in bilateral filter. The adaptive method can improve the restored image quality in this test result compared with standard bilateral filter. The proposed segmentation technique uses novel strip method and the image is split into number of strips 3, 4, 5 and 6. Using a hybrid Assured Convergence PSO (ACPSO) and Fuzzy C-Mean Clustering (FCM) was proposed method. The segmentation algorithm presented in this research gives 95% of accuracy rate to detect brain tumor when strip count is 5. In this research work presented a feature vector using a different statistical texture analysis of brain tumor from MRI image. The statistical feature texture is computed using GLCM (Gray Level Co-occurrence Matrices) of Brain Nodule structure. For this research work, the brain nodule segmented using strips method to implemented marker watershed image segmentation based on PSO (Particle Swarm Optimization) and Fuzzy C-means clustering (FCM). Furthermore, the four angles 0o, 45o, 90o and 135o are calculated the segmented brain image in GLCM. The four angular directions are calculated using texture features are correlation, energy, contrast and homogeneity. The accuracy rate of previous method was compared and proved the proposed method an Assured Convergence Particle Swarm Optimization (ACPSO)-Fuzzy C-Mean (FCM) and using SVM classification technique is suitable for the early detection of brain tumor. In proposed, a tumor extraction is improved in ASPSO-FCM and SVM classification with better accuracy rate of 95.31%.

Brain tumor detection using statistical and machine learning method

Background and ObjectiveBrain tumor occurs because of anomalous development of cells. It is one of the major reasons of death in adults around the globe. Millions of deaths can be prevented through early detection of brain tumor. Earlier brain tumor detection using Magnetic Resonance Imaging (MRI) may increase patient's survival rate. In MRI, tumor is shown more clearly that helps in the process of further treatment. This work aims to detect tumor at an early phase. MethodsIn this manuscript, Weiner filter with different wavelet bands is used to de-noise and enhance the input slices. Subsets of tumor pixels are found with Potential Field (PF) clustering. Furthermore, global threshold and different mathematical morphology operations are used to isolate the tumor region in Fluid Attenuated Inversion Recovery (Flair) and T2 MRI. For accurate classification, Local Binary Pattern (LBP) and Gabor Wavelet Transform (GWT) features are fused. ResultsThe proposed approach is evaluated in terms of peak signal to noise ratio (PSNR), mean squared error (MSE) and structured similarity index (SSIM) yielding results as 76.38, 0.037 and 0.98 on T2 and 76.2, 0.039 and 0.98 on Flair respectively. The segmentation results have been evaluated based on pixels, individual features and fused features. At pixels level, the comparison of proposed approach is done with ground truth slices and also validated in terms of foreground (FG) pixels, background (BG) pixels, error region (ER) and pixel quality (Q). The approach achieved 0.93 FG and 0.98 BG precision and 0.010 ER on a local dataset. On multimodal brain tumor segmentation challenge dataset BRATS 2013, 0.93 FG and 0.99 BG precision and 0.005 ER are acquired. Similarly on BRATS 2015, 0.97 FG and 0.98 BG precision and 0.015 ER are obtained. In terms of quality, the average Q value and deviation are 0.88 and 0.017. At the fused feature based level, specificity, sensitivity, accuracy, area under the curve (AUC) and dice similarity coefficient (DSC) are 1.00, 0.92, 0.93, 0.96 and 0.96 on BRATS 2013, 0.90, 1.00, 0.97, 0.98 and 0.98 on BRATS 2015 and 0.90, 0.91, 0.90, 0.77 and 0.95 on local dataset respectively. ConclusionThe presented approach outperformed as compared to existing approaches.

An Enhancement of Deep Learning Algorithm for Brain Tumor Segmentation Using Kernel Based CNN with M-SVM.

The brain tumor can be created by uncontrollable increase of abnormal cells in tissue of brain and it has two kinds of tumors: one is benign and another one is malignant tumor. The benign brain tumor does not affect the adjacent normal and healthy tissue but the malignant tumor can affect the neighboring tissues of brain that can lead to the death of person. An early detection of brain tumor can be required to protect the survival of patients. Usually, the brain tumor is detected using MRI scanning method. However, the radiologists are not providing the effective tumor segmentation in MRI image due to the irregular shape of tumors and position of tumor in the brain. Accurate brain tumor segmentation is needed to locate the tumor and it is used to give the correct treatment for a patient and it provides the key to the doctor who must execute the surgery for patient. In this paper, a novel deep learning algorithm (kernel based CNN) with M-SVM is presented to segment the tumor automatically and efficiently. This presented work contains several steps that are preprocessing, feature extraction, image classification and tumor segmentation of brain. The MRI image is smoothed and enhanced by Laplacian of Gaussian filtering method (LoG) with Contrast Limited Adaptive Histrogram Equalization (CLAHE) and feature can be extracted from it based on tumor shape position, shape and surface features in brain. Consequently, the image classification is done using M-SVM depending on the selected features. From MRI image, the tumor is segmented with help of kernel based CNN method.. Experimental results of proposed method can show that this presented technique can executes brain tumor segmentation accurately reaching almost 84% in evaluation with existing algorithms.

Use of machine intelligence to conduct analysis of human brain data for detection of abnormalities in its cognitive functions

The physical appearance of a brain tumor in human beings may be an indication of problems in psychological (cognitive) functions. Such functions include learning, understanding, problem solving, decision making, and planning. Early brain tumor detection can be done by using the proper procedure of screening. MRI is used for the detection of disease staging and follow-up without ionization radiation. In this manuscript, an automated system is proposed for the analysis of brain data and detection of cognitive functions abnormalities. The region of interest (ROI) is enhanced using a proposed partial differential diffusion filter (PDDF) which is a modified form of anisotropic diffusion filter. Otsu algorithm is used for better segmentation. Moreover, a new method is also proposed for feature extraction which is a concatenation of local binary pattern (LBP) and Gray level co-occurrence matrix (C2LBPGLCM). The proposed method accurately distinguishes between healthy and unhealthy images with high specificity, sensitivity, and area under the curve.