Brain Tumor Classification System Research Articles

Design and assessment of improved Convolutional Neural Network based brain tumor segmentation and classification system

Design and assessment of improved Convolutional Neural Network based brain tumor segmentation and classification system

RETRACTED ARTICLE: An optimized eagle adaboost model for brain tumor classification and severity analysis system

RETRACTED ARTICLE: An optimized eagle adaboost model for brain tumor classification and severity analysis system

Least square-support vector machine based brain tumor classification system with multi model texture features

Radiologists confront formidable challenges when confronted with the intricate task of classifying brain tumors through the analysis of MRI images. Our forthcoming manuscript introduces an innovative and highly effective methodology that capitalizes on the capabilities of Least Squares Support Vector Machines (LS-SVM) in tandem with the rich insights drawn from Multi-Scale Morphological Texture Features (MMTF) extracted from T1-weighted MR images. Our methodology underwent meticulous evaluation on a substantial dataset encompassing 139 cases, consisting of 119 cases of aberrant tumors and 20 cases of normal brain images. The outcomes we achieved are nothing short of extraordinary. Our LS-SVM-based approach vastly outperforms competing classifiers, demonstrating its dominance with an exceptional accuracy rate of 98.97%. This represents a substantial 3.97% improvement over alternative methods, accompanied by a notable 2.48% enhancement in Sensitivity and a substantial 10% increase in Specificity. These results conclusively surpass the performance of traditional classifiers such as Support Vector Machines (SVM), Radial Basis Function (RBF), and Artificial Neural Networks (ANN) in terms of classification accuracy. The outstanding performance of our model in the realm of brain tumor diagnosis signifies a substantial leap forward in the field, holding the promise of delivering more precise and dependable tools for radiologists and healthcare professionals in their pivotal role of identifying and classifying brain tumors using MRI imaging techniques.

EFF_D_SVM: a robust multi-type brain tumor classification system.

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.

Improvements to the Brain Tumor Segmentation and Classification System Using Convolutional Neural Networks

Improvements to the Brain Tumor Segmentation and Classification System Using Convolutional Neural Networks

Attention transformer mechanism and fusion-based deep learning architecture for MRI brain tumor classification system

Most primary brain malignancies are malignant tumors characterized by masses of abnormal tissue that grow uncontrollably. Recently, deep transfer learning (DTL) has been considered in the automated clinical application of magnetic resonance imaging (MRI) for determining brain tumor characteristics. Several previous studies have shown that tumors analyzed in MR images usually have local features, are extensive, and are associated with a high level of uncertainty. Accordingly, we introduce a two-branch parallel model that integrates the Transformer Module (TM) with the Self-Attention Unit (SAU) and Convolutional Neural Networks (CNN) to classify brain tumors in MR images. We also propose a novel approach that combines local and global features derived from CNNs and TMs to improve classification accuracy through a cross-fusion strategy. Hybrid architecture combined with cross-fusion allows parallel systems to be merged between branches, resulting in a pattern that identifies various types of tumors. Additionally, we developed a lightweight and improved CNN architecture (iResNet) that distinguishes tumor features based on MR images. From the 3064 slices in the four-class MR images from the Figshare dataset, 20 % were considered unseen images. The remainder was divided into 60 %, 20 %, and 20 % slices for training, validation, and testing. The overall structure was combined with other similar CNN networks (i.e., DenseNet, VGG, and ResNet), and we found that the accuracy of iVGG, iDensNet, and iResNet is 98.59, 98.94, and 99.30 %, respectively. Using local and global features, we developed an accurate and generalizable model to detect brain tumors in MRI, allowing rapid and accurate diagnosis.

Brain Tumor Detection by using Fine-tuned MobileNetV2 Deep Learning Model

Most of the deaths in the world happen due to Cancer. It is a disease in which the cells of our body organs or tissues grow in an undisciplined way which in turn can harm our normal cells and tissues in our body. These cells very smartly trick the immune system so that the cancerous cells are kept alive and are not destroyed. In the human body, tumors can be classified into three types: cancerous, non-cancerous, and pre-cancerous. Timely identification of the cancer can be helpful in many ways. As it improves a patient’s chances of survival. The most valuable, uncomplicated technique used is MRI scans for predicting tumor is a tough task and have chances of human error. So to be more accurate with our predictions we have moved on to use computerized techniques to ease the work. The focus of this research is the development of an automated brain tumor classification system using magnetic resonance imaging (MRI) scans, leveraging a deep learning model. The proposed model employs a convolutional neural network (CNN) architecture known as MobileNetV2, which is trained on a pre-processed MRI image dataset to classify brain tumors into one of two categories: tumor tissues and normal brain tissue. To mitigate overfitting and expand the dataset, data augmentation techniques are employed. The trained model achieves high accuracy, sensitivity, and specificity in classifying brain tumors. Proposed CNN model outperformed other deep learning models, including VGG16, Xception, and ResNet50, which were used for comparison.

BRAIN MRI SEGMENTATION MODELLING USING DEEP LEARNING

The human body is made up of many organs and brain is the most critical and vital organ of them all. One of the common reasons for dysfunction of brain is brain tumor. A tumor is nothing but excess cells growing in an uncontrolled manner Currently, doctors locate the position and the area of brain tumor by looking at the MR Images of the brain of the patient manually. This results in inaccurate detection of the tumor and is considered very time consuming. The brain tumor detection and classification system is available so that it can be diagnosed at early stages. Cancer classification is the most challenging tasks in clinical diagnosis. This project deals with such a system, which uses computer, based procedures to detect tumor blocks and classify the type of tumor using Convolution Neural Network Algorithm for MRI images of different patients. Different types of image processing techniques like image segmentation, image enhancement and feature extraction are used for the brain tumor detection in the MRI images of the cancer-affected patients. Detecting Brain tumor using Image Processing techniques its involves the four stages is Image Pre-Processing, Image segmentation, Feature Extraction, and Classification. Image processing and neural network techniques are used for improve the performance of detecting and classifying brain tumor in MRI images.

Convolutional Gated Recurrent Neural Network Based Automatic Detection and Classification of Brain Tumor using Magnetic Resonance Imaging

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%.

Neuropathology Outreach in Ghana

Abstract Introduction/Objective Ghana has a population of over 31 million people and approximately 15 neurosurgeons but is currently without a dedicated neuropathologist. Here we present a month of neuropathology outreach performed at the Komfo Anokye Teaching Hospital (KATH) in Kumasi and the Korle-Bu Teaching Hospital (KBTH) in Accra. Methods/Case Report In February of 2022, a second-year neuropathology fellow and a board-certified neuropathologist traveled to Ghana to give 40 approximately 1.5 hour neuropathology didactic and interactive teaching sessions. A two-week format of outreach was developed for each site in Ghana and consisted of didactic lectures and interactive sessions covering the WHO 2021 brain tumor classification system, neuroautopsy, neuroinfectious disease, neurodegenerative disease, neuromuscular disease, forensic neuropathology, neurodevelopment, and unknown case conferences. Meanwhile, a pathology trainee worked remotely to organize 12 one-hour virtual general pathology lectures and develop continuing outreach. A survey of 16 questions including 12 multiple choice questions and four short answer questions was used to assess each component and the overall satisfaction of the outreach. Results (if a Case Study enter NA) Eight brains were examined at four neuroautopsy conferences and ten case consultations were performed upon request. Survey results showed no statistical differences amongst the 20 sessions. Comments were positive and included one respondent planning to redesign their PhD research to focus on neurodegenerative disease in Ghana and several others commenting on the utility of molecular updates. Suggestions for improvement included quizzes, including more morphology and IHC in lectures, sending digital slides before future visits, teaching spinal cord removal, and the need for continuing outreach in neuropathology, dermatopathology, cytopathology and gynecologic pathology. Conclusion This experience provides a useful and well-appreciated approach to delivering international neuropathology outreach. Opportunities in neuropathology on an international level range from educational to clinical practice and research.

A Radiologist's Guide to the 2021 WHO Central Nervous System Tumor Classification: Part I-Key Concepts and the Spectrum of Diffuse Gliomas.

The fifth edition of the World Health Organization (WHO) classification of tumors of the central nervous system, published in 2021, contains substantial updates in the classification of tumor types. Many of these changes are relevant to radiologists, including "big picture" changes to tumor diagnosis methods, nomenclature, and grading, which apply broadly to many or all central nervous system tumor types, as well as the addition, elimination, and renaming of multiple specific tumor types. Radiologists are integral in interpreting brain tumor imaging studies and have a considerable impact on patient care. Thus, radiologists must be aware of pertinent changes in the field. Staying updated with the most current guidelines allows radiologists to be informed and effective at multidisciplinary tumor boards and in interactions with colleagues in neuro-oncology, neurosurgery, radiation oncology, and neuropathology. This review represents the first of a two-installment review series on the most recent changes to the WHO brain tumor classification system. This first installment focuses on the changes to the classification of adult and pediatric gliomas of greatest relevance for radiologists.

Brain tumor identification and classification system using convolutional neural network

The production of extra cells often results in the formation of clusters tissue, which means growth or tumor. Brain tumor has two types: Benign Non-Cancer and Malignant Cancer and Leading bad human condition. Brain tumor has various dimensions as well shapes and comes from different places. It is very important to find the type it grows at its beginning. Separation, acquisition and acquisition of the location of the infected tumor from magnetic resonance imaging (MRI) is primary anxiety but a tedious and time-consuming task done by a radiologist or clinic doctor professionals and their accuracy is based solely on their experience. So use computer technology required. So feeding ahead of performance. A neural network called the Convolution neural network (CNN) is used for measurement the complexity of the tumor in the brain that provides accurate results.

Diagnosis of Brain Tumor Using Artificial Neural Network

Accurate detection and diagnosis of brain tumor is one the crucial task of medical image analysis. Brain tumor classification system aids the physician to make accurate diagnosis and to provide effective treatment. Magnetic Resonance Imaging (MRI) is the gold standard imaging technique for brain tumor diagnosis. This paper proposes a method for brain tumor detection and classification using artificial neural network. The proposed method consists of four major processes such as preprocessing, region of interest segmentation, feature extraction and classification. Feed forward neural network is employed to classify the brain tumors. Classification performance of the proposed method is evaluated using 10-cross fold validation and compared with the previous methods. Empirical findings proved that the proposed method can efficiently classify the brain tumor with higher classification rate.

MRI brain tumor detection using optimal possibilistic fuzzy C-means clustering algorithm and adaptive k-nearest neighbor classifier

Brain tumor characterizes the aggregation of abnormal cells in specific tissues of the brain zone. The prior distinguishing proof of brain tumors has a huge influence on the treatment and recovery of the patient. The identification of a brain tumor and its evaluation is commonly a troublesome and tedious assignment. For effective classification and grading of brain tumor images, in this paper, we present an automatic MRI brain tumor classification system. The proposed work consists of four modules namely, pre-processing, feature extraction, classification, and segmentation. Initially, the noise present in the input image is removed using the Median Filter because the noises present in the input images will affect the accuracy of the classification process. At once, the images are converted into 3 × 3 blocks. Then, the texture features are extracted from the pre-processed image. After the feature extraction process, the features are given to the adaptive k-nearest neighbor classifier to classify an image as normal or abnormal. Later, the tumor regions are segmented with the help of the optimal possibilistic fuzzy C-means clustering algorithm. Both classification and the segmentation appearance technique are evaluated in terms of accuracy; sensitivity as well as specificity. For experimental analysis two dataset are utilized namely, BRATS MICCAI brain tumor dataset and publically available dataset.

Low Volume Brain Tumor Extraction

The segmentation of magnetic resonance images plays a very important role in medical fields because it extracts the required area from the image. By and large there is no novel methodology for the division of picture. Tumor segmentation from MRI data is an important but time consuming manual task performed by medical experts. This paper focuses on a new and very famous algorithm for brain tumor segmentation of MRI image by ANN algorithm to diagnose accurately the region of cancer because of its simplicity and computational efficiency. The MATLAB output will be displayed in pc and also see the output to embedded system using wired communication. Brain tumor is a life threatening disease. The cerebrum contains in excess of 10 billion working mind cells. The damaged brain cells are diagnosed themselves by splitting to make more cells. This regeneration takes place in an orderly and controlled manner. If the regeneration of the cells gets out of control, the cells will continue to divide developing a lump which is called tumor. In this paper a Brain Tumor Detection and Classification System has been designed and developed. The framework utilizes PC based strategies to recognize tumor squares and order the sort of tumor utilizing Artificial Neural Network in MRI pictures of various patients with astrocytoma kind of mind tumors.

Parasagittal Meningioma Brain Tumor Classification System Based on Mri Images and Multi Phase Level Set Formulation

The most common type of brain tumor known as Meningioma arises from the meninges and encloses the spine and the brain inside the skull. It accounts for 30% of all types of brain tumor. Meningioma’s can occur in many parts of the brain and accordingly it is named. In this paper, we propose Meningioma brain tumor classification system using MRI image is developed . Firstly, based on the characteristics of MRI image and Chan-Vese model, we use multiphase level set method to get the interesting region. Therefore, we obtain two matrixes, in which one contains the whole cell's boundary, and the other contains the boundary of some cells. Secondly, with respect to the cells' boundary, it is necessary to further processing, which ensures the boundary of some cells is a discrete region. Mathematical Morphology brings a fancy result during the discrete processing. At last, we consider every discrete region according to the tumor's features to judge whether a tumor appears in the image or not. Our method has a desirable performance in the presence of common tumors. For some non-convex tumors, we utilized a traditional way (SVM and LBP) as a second processing, which increased the coverage and accuracy. Experiments show that our method has a high coverage without any learning-based classifiers for most common tumors, which saves a lot time and reduces a lot workload. Therefore, the proposed method has a good practical application for assisting physicians in detecting Meningiom tumors using MRI images.

Neural Network Based Brain Tumor Detection Using Wireless Infrared Imaging Sensor

Now-a-days image processing placed an important role for recognizing various diseases such as breast, lung, and brain tumors in earlier stage for giving the appropriate treatment. Presently, most cancer diagnosis worked according to the visual examination process with effectively. Human visual reviewing of infinitesimal biopsy pictures is exceptionally tedious, subjective, and conflicting due to between and intra-onlooker varieties. In this manner, the malignancy and it’s compose will be distinguished in a beginning time for finish treatment and fix. This brain tumor classification system using machine learning-based back propagation neural networks (MLBPNN) causes pathologists to enhance the exactness and proficiency in location of threat and to limit the entomb onlooker variety. Moreover, the technique may assist doctors with analyzing the picture cell by utilizing order and bunching calculations by recoloring qualities of the phones. The different picture preparing steps required for disease location from biopsy pictures incorporate procurement, upgrade, and division; include extraction, picture portrayal, characterization, and basic leadership. In this paper, MLBPNN is analyzed with the help of infra-red sensor imaging technology. Then, the computational multifaceted nature of neural distinguishing proof incredibly diminished when the entire framework is deteriorated into a few subsystems. The features are extracted using fractal dimension algorithm and then the most significant features are selected using multi fractal detection technique to reduce the complexity. This imaging sensor is integrated via wireless infrared imaging sensor which is produced to transmit the tumor warm data to a specialist clinician to screen the wellbeing condition and for helpful control of ultrasound measurements level, especially if there should arise an occurrence of elderly patients living in remote zones.

Multi-grade brain tumor classification using deep CNN with extensive data augmentation

Numerous computer-aided diagnosis (CAD) systems have been recently presented in the history of medical imaging to assist radiologists about their patients. For full assistance of radiologists and better analysis of magnetic resonance imaging (MRI), multi-grade classification of brain tumor is an essential procedure. In this paper, we propose a novel convolutional neural network (CNN) based multi-grade brain tumor classification system. Firstly, tumor regions from an MR image are segmented using a deep learning technique. Secondly, extensive data augmentation is employed to effectively train the proposed system, avoiding the lack of data problem when dealing with MRI for multi-grade brain tumor classification. Finally, a pre-trained CNN model is fine-tuned using augmented data for brain tumor grade classification. The proposed system is experimentally evaluated on both augmented and original data and results show its convincing performance compared to existing methods.

WHO-Klassifikation der Hirntumoren 2016: radiologisch relevante Neuerungen

AbstractRadiologists play a key role in brain tumor diagnosis and management and must stay abreast of developments in the field to advance patient care and communicate with other health care providers. In 2016, the World Health Organization (WHO) released an update to its brain tumor classification system that included numerous significant changes. Several previously recognized brain tumor diagnoses, such as oligoastrocytoma, primitive neuroectodermal tumor, and gliomatosis cerebri, were redefined or eliminated altogether. Conversely, multiple new entities were recognized, including diffuse leptomeningeal glioneuronal tumor and multinodular and vacuolating tumor of the cerebrum. The glioma category has been significantly reorganized, with several infiltrating gliomas in children and adults now defined by genetic features for the first time. These changes were driven by increased understanding of important genetic factors that directly impact tumorigenesis and influence patient care. The increased emphasis on genetic factors in brain tumor diagnosis has important implications for radiology, as we now have tools that allow us to evaluate some of these alterations directly, such as the identification of 2-hydroxyglutarate within infiltrating gliomas harboring mutations in the genes for the isocitrate dehydrogenases. For other tumors, such as medulloblastoma, imaging can demonstrate characteristic patterns that correlate with particular disease subtypes. The purpose of this article is to review the changes to the WHO brain tumor classification system that are most pertinent to radiologists.

Deep learning based brain tumor classification and detection system

Deep learning based brain tumor classification and detection system