Brain Tumour Categorization with MRI Representation by Feature Extraction using GLCM and Support Vector Machine

A brain tumor occurs when abnormal cells form within the brain. Many people suffer from brain tumor, and it is a serious and dangerous disease. The detection and classification of brain tumor is an important and difficult task in the medical field. The brain tumor detection technique in the MRI images is very significant in many symptomatic and cure applications. But, as the data in MRI images are of highquality, tumor detection and classification are very hard in this process. Medical imaging plays a major role in properly diagnosing the disease, wherein an essential part in detecting the tumor is image segmentation. The segmentation provides an automatic brain tumor detection technique in order to increase the precision of the yields while also decreasing the diagnosis time. The goal is to detect the tumor from the MRI images and extract the features from the segmented tumor and finally classify it. The image detection and classification process includes image acquisition, image pre-processing, denoising, image segmentation, feature extraction and classification. The input image is pre-processed using Weiner and median filtering and the noise is removed using Edge Adaptive Total Variation Denoising (EATVD) technique. Once the noise is removed from the image, it is used for segmentation process, where Mean Shift Clustering is used. The segmented tumor undergoes features extraction stage, where Gray Level Co- occurrence Matrix (GLCM) features are used. In the last stage, images are classified either as tumorous or non-tumorous. Classification is done using Genetic Algorithm Support Vector Machine (GA-SVM), Deep Learning with Convolutional Neural Network (CNN). Early detection of the tumor region can be achieved without much time lapse in the calculation by using this efficient classifier model. This system presents a prototype for detecting objects based on GA-SVM that classifies images and assesses whether the image is cancerous. While comparing the accuracy and computational time of these classifiers, CNN would provide high accuracy and GASVM with lesser simulation time. The simulation results obtained for brain tumor detection and analysis are done with minimum computational time and with reasonable accuracy. This proposed system is tested using SPL dataset, which consists of 20 cases with 40 image samples of T2 FLAIR weighted MRI image and implemented using MATLAB software.

Accurate detection and classification of brain tumors are essential for timely diagnosis and effective treatment planning. This study presents an integrated framework leveraging both machine learning (ML) and deep learning (DL) models for brain tumor detection and classification using MRI images. Two publicly available datasets are utilized: one for binary classification (tumor vs. no tumor) and another for multiclass classification (glioma, meningioma, and pituitary tumors). Comprehensive preprocessing steps, including resizing, feature extraction using the Gray Level Co-occurrence Matrix (GLCM), and feature selection via Chi-square testing, were employed to optimize the dataset for modeling. Machine learning models such as Decision Trees, K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and AdaBoost were compared with deep learning architectures like Convolutional Neural Networks (CNNs) and the pre-trained VGG16 model. Hyperparameter optimization techniques, including grid search and the Adam optimizer, were used to enhance model performance. The models were evaluated using metrics such as accuracy, precision, recall, F1-score, Mean Squared Error (MSE), and Mean Absolute Error (MAE). Results indicate that the VGG16 model consistently outperformed other approaches, achieving high validation accuracy. This study highlights the potential of integrating ML and DL techniques for accurate and efficient brain tumor detection and classification, offering valuable tools for medical diagnostics.

Brain tumour classification using conventional techniques involving human intervention is prone to errors. Errors in feature extraction and/or classification may turn out to be fatal. Hence focus has shifted on automated techniques for the feature extraction and classification of brain tumour images. In the present work, a mechanism for the same has been proposed involving Artificial Neural Networks (ANN). Three categories have been incorporated in the present work, viz. normal brain, brain with benign tumour and brain with malignant tumour. In the present case, MRI images of the brain have been used to train an Artificial Neural Network which subsequently predicts the category of some new MRI data. The key challenge in designing such a system is attaining high accuracy of classification. This can be achieved by accurate feature extraction mechanism and then designing an appropriate Neural Network for training and testing. At the outset, segmentation has been employed to separate the region of the tumour (if any). Later the Discrete Wavelet Transform (DWT) is used as a preprocessing tool. The DWT helps in removing the non linearities of the image signal and smoothen it out. Since images exhibit abrupt changes in pixel values, conventional Fourier methods like the Fourier Transform prove to be unfit for image analysis. Subsequently, features like contrast, correlation, energy, homogeneity, mean, standard deviation, entropy, RMS value, variance, smoothness, kurtosis and skewness have been evaluated. Prior to training the Neural Network using the feature values, Principal Component Analysis (PCA) has been employed to find trends in the feature values. A Probabilistic Neural Network (PNN) has been designed and it has been trained using the feature values. I has been found that brain tumour MRI image are classified into normal, benign and malignant category using PNN classifier with an accuracy of 97% for the used data set which can be attributed to the efficacy of the proposed method. Key Words: Brain Tumor Classification, Machine Learning, Feature Extraction, Neural Networks, Confusion Matrix, Classification Accuracy.

Background and Objectives: Brain Tumor Fusion-based Segments and Classification-Non-enhancing tumor (BTFSC-Net) is a hybrid system for classifying brain tumors that combine medical image fusion, segmentation, feature extraction, and classification procedures. Materials and Methods: to reduce noise from medical images, the hybrid probabilistic wiener filter (HPWF) is first applied as a preprocessing step. Then, to combine robust edge analysis (REA) properties in magnetic resonance imaging (MRI) and computed tomography (CT) medical images, a fusion network based on deep learning convolutional neural networks (DLCNN) is developed. Here, the brain images’ slopes and borders are detected using REA. To separate the sick region from the color image, adaptive fuzzy c-means integrated k-means (HFCMIK) clustering is then implemented. To extract hybrid features from the fused image, low-level features based on the redundant discrete wavelet transform (RDWT), empirical color features, and texture characteristics based on the gray-level cooccurrence matrix (GLCM) are also used. Finally, to distinguish between benign and malignant tumors, a deep learning probabilistic neural network (DLPNN) is deployed. Results: according to the findings, the suggested BTFSC-Net model performed better than more traditional preprocessing, fusion, segmentation, and classification techniques. Additionally, 99.21% segmentation accuracy and 99.46% classification accuracy were reached using the proposed BTFSC-Net model. Conclusions: earlier approaches have not performed as well as our presented method for image fusion, segmentation, feature extraction, classification operations, and brain tumor classification. These results illustrate that the designed approach performed more effectively in terms of enhanced quantitative evaluation with better accuracy as well as visual performance.

For determining the appropriate treatment for brain tumors, an accurate diagnosis is necessary. Many studies have focused on the deep learning-based classification of brain tumors. This study employed a comprehensive approach using deep learning techniques to improve diagnostic accuracy and treatment planning for brain tumors. The Random Forest algorithm, SVM, YOLOv5, and Neural Network classifier were utilized to classify brain tumors and different planes of brain MRI images. To segment the tumors, the U-Net architecture with various neural network backbones (VGG16, Resnet34, and InceptionV3) was implemented, and metrics such as intersection over union (IOU) and dice coefficient were used to assess the segmentation outcome. The U-Net model achieved an IOU of 0.73 and a dice coefficient of 0.84. The Random Forest algorithm achieved 99 percent and 95 percent classification accuracy in image planes and tumor types, respectively. The SVM, YOLOv5, and Neural Network Classifier provided classification accuracies of 91 percent, 95 percent, and 96 percent, respectively, in classifying brain tumors. The ensemble models predicted the final result, demonstrating the potential of this approach to improve brain tumor diagnosis and treatment planning.

Brain tumors are a growth of abnormal cells in the intracranial tissue that can disrupt proper brain function. In general, brain tumors are classified into two main categories, benign and malignant. This research aims to classify three types of benign tumors, that are Meningioma (Mg), Glioma (Gl), and Pituitary (Pt) from MRI images. The benign tumors types are classified into four data categories, that are Mg-Gl, Mg-Pt, Gl-Pt, Mg-GI-Pt. The Feature extraction uses Discrete Wavelet Transform (DWT) and Gray Level Co-Occurrence Matrix (GLCM) variant combination as a hybrid feature for recognize and classifying benign tumors types. The classification uses Convolutional Neural Network (CNN) method with ten layers structure. From our experiments, the average accuracy value of DWT combined with four GLCM features, that are Contrast, Homogeneity, Correlation, and Energy is 78.03% in all data categories.

Magnetic Resonance Imaging (MRI) might be a problematic assignment for tumor fluctuation and complexity because of brain image classification. This work presents the brain tumor classification system using Convolutional Gated Recurrent Neural Network (CGRNN) algorithm based on MRI images. The proposed tumor recognition framework comprises of four stages, to be specific preprocessing, feature extraction, segmentation and classification. Extraction of identified tumor framework features was accomplished utilizing Gray Level Co-occurrence Matrix (GLCM) strategy. At long last, the Convolutional Gated Recurrent Neural Network Classifier has been created to perceive various kinds of brain disease. The proposed framework can be effective in grouping these models and reacting to any variation from the abnormality. The entire framework is isolated into different types of phases: the Learning/Training Phase and the Recognition/Test Phase. A CGRNN classifier under the scholarly ideal separation measurements is utilized to decide the chance of every pixel having a place with the foreground (tumor) and the background. MATLAB software is used in the development of the simulation of the proposed system. The suggested method's simulation results show that the analysis of brain tumours is stable. It shows that the proposed brain tumor classifications are superior to those from brain MRIs than existing brain tumor classifications. The overall accuracy of the proposed system is 98.45%.

Here, a brain tumor classification method using the support vector machine (SVM) algorithm by utilizing discrete wavelet transform (DWT) transformation and feature extraction of gray-level co-occurrence matrix (GLCM) and local binary pattern (LBP) has been implemented using the magnetic resonance imaging (MRI) image belong to the low-grade glioma (LGG) or high-grade glioma (HGG) group. SVM algorithm used as a classification method has been widely used in research that raises the topic of classification. Through the formation of a hyperplane between 2 data classes, the SVM algorithm can be said to be a reliable method but does not require complicated computations. The DWT transformation is intended to provide clearer feature details from the MRI image, so that when the feature extraction algorithm is applied, it is expected that the extracted features will differ between benign tumor MRI images and malignant tumor MRI images. In 1 level DWT using high-low (HL) sub-band yield the highest specificity, sensitivity, and accuracy than using 3 levels using HL or low-high (LH) sub-band in LGG MRI image.Compared with another research, our proposed method is slightly better in terms of accuracy to classify the brain tumor image with achieved the accuracy of 98.6486%.

WITHDRAWN: MRI based detection and classification of brain tumor using enhanced faster R-CNN and Alex Net model

Brain tumor is the phenomenal growth of abnormal tissues in the human brain. A malignant tumor is known as a cancerous cell, and it is the major cause of death among people across the globe. Nowadays, the detection and classification of brain tumors from Magnetic Resonance Images (MRI) is a very crucial task. This chapter addresses various computing methods such as Edge Detection (ED), feature extraction, and classification techniques for the detection and classification of brain tumor regions from MRI datasets. Initially, MRI images are collected, and then various pre-processing steps such as filtering and edge detection are applied. Then, different edge detection methods such as Sobel ED, Robert’s ED, Prewitt ED, Canny ED, Laplacian ED, and Laplacian of Gaussian (LoG) with sigma 3 are applied for the MRI datasets. Subsequently, segmentation techniques are applied for detecting tumor regions from MRI, and essential features are extracted using Discrete Wavelet Transform (DWT) method. Otsu’s thresholding and K-means clustering segmentation methods are used for the investigation. Further, the support vector machines (SVMs), Naïve Bayes (NB), K-nearest neighbors (KNN), Back Propagation Neural Network (BPNN), and feedforward neural networks (FFNN) classifiers are employed for the MRI classification purpose. The experiments are 146conducted using the brain tumor dataset in the MATLAB 2019a software environment. The experimental results are analyzed in multiple dimensions, and it shows that the SVM with Otsu’s thresholding method exhibits better performance with 86.11% accuracy during the classification.

This research focuses on improving the detection and classification of brain tumors using a method called Brain Tumor Classification using Dual-Discriminator Conditional Generative Adversarial Network (DDCGAN) for MRI images. The proposed system is implemented in the MATLAB programming language. In this study, images of the brain are taken from a dataset and processed to remove noise and enhance image quality. The brain pictures are taken from Brats MRI image dataset. The images are preprocessed using Structural interval gradient filtering to remove noises and improve the quality of the image. The preprocessing outcomes are given to feature extraction. The features are extracted by Empirical wavelet transform (EWT) and the extracted features are given to the Dual-discriminator conditional generative adversarial network (DDCGAN) for recognizing the brain tumor, which classifies the brain images into glioma, meningioma, pituitary gland, and normal. Then, the weight parameter of DDCGAN is optimized by utilizing Border Collie Optimization (BCO), which is a met a heuristic approach to handle the real world optimization issues. It maximizes the detection accurateness and reduced computational time. Implemented in MATLAB, the experimental results demonstrate that the proposed system achieves a high sensitivity of 99.58%. The BCO-DDCGAN-MRI-BTC method outperforms existing techniques in terms of precision and sensitivity when compared to methods like Kernel Basis SVM (KSVM-HHO-BTC), Joint Training of Two-Channel Deep Neural Network (JT-TCDNN-BTC), and YOLOv2 including Convolutional Neural Network (YOLOv2-CNN-BTC). The research findings indicate that the proposed method enhances the accuracy of brain tumor classification while reducing computational time and errors.

Medical imaging is a field of technology which is mainly used to get referential images of human body for medical science and research work. These images are extremely useful for us. We can detect brain tumor in a human body with the help of these MRI Images collected as Big Data. The Automation in this particular area is one of the hot topics of research in Medical Science. These images help us in determining the exact location and area of the weird tissues. Its orientation can also be found out. We can use support vector machine (SVM) for classification, for brain tumor segmentation use fuzzy c-means(FCM) and discrete wavelet transform(DWT). To detect the suspicious region in brain MRI image we need to do clustering and then the features are extracted from the brain image. Feature extraction is a method of capturing visual content of an image. The MRI Images having tumor are downloaded from the internet and the experiment is done on them. After which the SVM technique is used for the classifying the MRI images of brain, which in turn gives a more efficient result with high accuracy for brain MRI image classification and finally the accuracy of both the segmentation algorithms is compared.

In this study we have proposed a hybrid algorithm for detection brain tumor in Magnetic Resonance images using statistical features and Fuzzy Support Vector Machine (FSVM) classifier. Brain tumors are not diagnosed early and cured properly so they will cause permanent brain damage or death to patients. Tumor position and size are important for successful treatment. There are several algorithms are developed for brain tumor detection and classifications in the field of medical image processing. The proposed technique consists of four stages namely, Noise reduction, Feature extraction, Feature reduction and Classification. In the first stage anisotropic filter is applied for noise reduction and to make the image suitable for extracting features. In the second stage, obtains the texture features related to MRI images. In the third stage, the features of magnetic resonance images have been reduced using principles component analysis to the most essential features. At the last stage, the Supervisor classifier based FSVM has been used to classify subjects as normal and abnormal brain MR images. Classification accuracy 95.80% has been obtained by the proposed algorithm. The result shows that the proposed technique is robust and effective compared with other recent works.

Due to the complexity of the images and dearth of anatomical models, it is highly difficult to accurately represent the various deformations in each component of the medical images. In recent years, a significant number of children and adults have affected from brain tumors, which is one of the most terrible types of disease affects the people around the world. Moreover, the Magnetic Resonance Imaging (MRI) based brain tumor detection is one of a significant study area in the field of medical imaging. Since, the use of computerized methods aids in the detection and treatment of disease by the medical professionals. The development of an automated method for the accurate detection and classification of tumors from brain MRIs. In this framework, a tanh normalization process is used to smooth out the input brain MRIs with less noise artefacts and improved quality. Then, a group feature extraction model is used to extract the relevant features from the normalized image, which includes both Speeded Up Robust Features (SURF) and Grey Level Co-occurrence Matrix (GLCM) features. The Water Chaotic Fruitfly Optimization (WChFO) method is used to identify the best features for increasing the speed of classifier training and testing processes with less time. Moreover, a Deep Recurrent Neural Network (DRNN) model is used to classify the type of brain tumor for accurate early diagnosis and treatment. The most well-known benchmarking datasets, like BRATS and Kaggle, employed for analysis in order to assess the effectiveness and results of the proposed brain tumor diagnosis system. By using the proposed WChFO-DRNN technique, the accuracy of the tumor detection system is increased to 99.2% with the sensitivity, specificity of 99% and time consumption of 0.2s.

Brain tumors are one of the most threatening diseases to human health. Accurate identification of the type of brain tumor is essential for patients and doctors. An automated brain tumor diagnosis system based on Magnetic Resonance Imaging (MRI) can help doctors to identify the type of tumor and reduce their workload, so it is vital to improve the performance of such systems. Due to the challenge of collecting sufficient data on brain tumors, utilizing pre-trained Convolutional Neural Network (CNN) models for brain tumors classification is a feasible approach. The study proposes a novel brain tumor classification system, called EFF_D_SVM, which is developed on the basic of pre-trained EfficientNetB0 model. Firstly, a new feature extraction module EFF_D was proposed, in which the classification layer of EfficientNetB0 was replaced with two dropout layers and two dense layers. Secondly, the EFF_D model was fine-tuned using Softmax, and then features of brain tumor images were extracted using the fine-tuned EFF_D. Finally, the features were classified using Support Vector Machine (SVM). In order to verify the effectiveness of the proposed brain tumor classification system, a series of comparative experiments were carried out. Moreover, to understand the extracted features of the brain tumor images, Grad-CAM technology was used to visualize the proposed model. Furthermore, cross-validation was conducted to verify the robustness of the proposed model. The evaluation metrics including accuracy, F1-score, recall, and precision were used to evaluate proposed system performance. The experimental results indicate that the proposed model is superior to other state-of-the-art models.