What are brain tumors?

Using biopsy, brain tumor classification is performed, which is not normally conducted before definitive brain surgery. Technology improvement and machine learning help radiologists with the diagnostics of tumors without invasive measures. Convolutional neural network (CNN) is the machine-learning algorithm that achieved substantial results in image classification and segmentation. Some of the most notable primary brain tumors are meningiomas, gliomas, and pituitary tumors. Gliomas is a general term for a tumor that arises from brain tissues other than nerve cells and blood vessels. But, meningiomas arise from membranes that cover the brain and surround the central nervous system, whereas pituitary tumors are the lumps that sit inside the skull. The most notable important difference between these three types is that meningiomas are generally benign, and gliomas are commonly malignant. This project develops a new CNN architecture to classify brain tumor types. With i) good generalization capability and ii) good execution speed, the newly developed CNN architecture is being used as an effective decision-support tool for radiologists in diagnostics. Python is used for the development of the project. Keywords: Deep Learning, Neural Network, Brain Tumor, MRI Images.

Brain tumor is one of the leading disease in the world. So automated identification and classification of tumors are important for diagnosis. Magnetic resonance imaging (MRI)is widely used modality for imaging brain. Brain tumor classification refers to classify the brain MR images as normal or abnormal, benign or malignant, low grade or high grade or types. This paper reviews various techniques used for the classification of brain tumors from MR images. Brain tumor classification can be divided into three phases as preprocessing, feature extraction and classification. As segmentation is not mandatory for classification, hence resides in the first phase. The feature extraction phase also contains feature reduction. DWT is efficient for both preprocessing and feature extraction. Texture analysis based on GLCM gives better features for classification where PCA reduces the feature vector maintaining the accuracy of classification of brain MRI. Shape features are important where segmentation has already been performed. The use of SVM along with appropriate kernel techniques can help in classifying the brain tumors from MRI. High accuracy has been achieved to classify brain MRI as normal or abnormal, benign or malignant and low grade or high grade. But classifying the tumors into more particular types is more challenging.

Brain tumor classification is a significant task for evaluating tumors and selecting the type of treatment as per their classes. Brain tumors are diagnosed using multiple imaging techniques. However, MRI is frequently utilized since it provides greater image quality and uses non-ionizing radiation. Deep learning (DL) is a subfield of machine learning and recently displayed impressive performance, particularly in segmentation and classifying problems. Based on convolutional neural network (CNN), a Hybrid Deep Learning Network (HDLN) model is proposed in this research for classifying multiple types of brain tumors including glioma, meningioma, and pituitary tumors. The Mask RCNN is used for brain tumor classification. We used a squeeze-and-excitation residual network (SE-ResNet) for brain tumor segmentation, which is a residual network (ResNet) with a squeeze-and-excitation block. A publicly available research dataset is used for testing the proposed model for experiment analysis and it obtained an overall accuracy of 98.53%, 98.64% sensitivity and 98.91% specificity. In comparison to the most advanced classification models, the proposed model obtained the best accuracy. For multi-class brain tumor diseases, the proposed HDLN model demonstrated its superiority to the existing approaches.

Brain tumor classification is crucial for medical evaluation in computer-assisted diagnostics (CAD). However, manual diagnosis of brain tumors from magnetic resonance imaging (MRI) can be time-consuming and complex, leading to inaccurate detection and classification. This is mainly because brain tumor identification is a complex procedure that relies on different modules. The advancements in Deep Learning (DL) have assisted in the automated process of medical images and diagnostics for various medical conditions, which benefits the health sector. Convolutional Neural Network (CNN) is one of the most prominent DL methods for visual learning and image classification tasks. This study presents a novel CNN algorithm to classify the brain tumor types of glioma, meningioma, and pituitary. The algorithm was tested on benchmarked data and compared with the existing pre-trained VGG16, VGG19, ResNet50, MobileNetV2, and InceptionV3 algorithms reported in the literature. The experimental results have indicated a high classification accuracy of 98.04%, precision, recall, and f1-score success rate of 98%, respectively. The classification results proved that the most common kinds of brain tumors could be categorized with a high level of accuracy. The presented algorithm has good generalization capability and execution speed that can be helpful in the field of medicine to assist doctors in making prompt and accurate decisions associated with brain tumor diagnosis.

Brain tumor means the aggregation of abnormal cells in some tissues of the brain. Brain tumor can be cancerous or noncancerous. The most common types of brain tumors are Glioma, Meningioma and Pituitary tumor. Early detection of tumor cells plays a major role in treatment and recovery of patient. Diagnosing a brain tumor usually undergoes a very complicated and time consuming process. The MRI images of various patients at various stages can be used for the detection of tumors. There are various types of feature extraction and classification methods which are used for detection of brain tumor from MRI images. Convolutional Neural Network image classification algorithm helps in detecting the tumor at early stage with high accuracy. We proposed a Recurrent Neural Network architecture for detection of tumor cells which gives accuracy of about 90%. A recurrent neural network (RNN) is a type of artificial neural networks in that connections between nodes form a directed graph along a temporal sequence.

Brain tumor has been acknowledged as the most dangerous disease through all its circles. Early identification of tumor disease is considered pivotal to identify the spread of brain tumors in administering the appropriate treatment. This study proposes a Convolutional Neural Network method to detect brain tumor on MRI images. The 3264 datasets were undertaken in this study with detailed images of Glioma tumor (926 images), Meningioma tumors (937 images), pituitary tumors (901 images), and other with no-tumors (500 images). The application of CNN method combined with Hyperparameter Tuning is proposed to achieve optimal results in classifying the brain tumor types. Hyperparameter Tuning acts as a navigator to achieve the best parameters in the proposed CNN model. In this study, the model testing was conducted with three different scenarios. The result of brain tumor classification depicts an accuracy of 96% in the third model testing scenario.

A brain tumor is an abnormal growth of cells in the brain. There are four common types of brain tumors.  Doctors can segment and identify the tumors manually, but it is very time-consuming. There exist automatic segmentation algorithms that can facilitate the process. Deep learning is a new method of creating powerful AI models. As a result, there is a need for automatic segmentation algorithms that can facilitate the process and improve the accuracy of brain tumor detection. Artificial intelligence (AI) and machine learning (ML) have emerged as promising tools for developing such algorithms. In particular, deep learning (DL) methods, such as convolutional neural networks (CNNs), have shown great potential for accurately identifying brain tumors in medical images. This paper presents a literature review of recently published papers (2020-2022) on brain tumor classification and detection using artificial intelligence. The review covers various AI and DL methods, including supervised learning, reinforcement learning, and unsupervised learning. It evaluates their effectiveness in detecting and classifying brain tumors in medical images. The review also discusses the challenges and limitations of these methods, as well as future directions for research in this field.

Brain tumor is a common and fatal form of cancer which affects both adults and children. The classification of brain tumors into different types is hence a crucial task, as it greatly influences the treatment that physicians will prescribe. In light of this, medical imaging techniques, especially those applying deep convolutional networks followed by a classification layer, have been developed to make possible computer-aided classification of brain tumor types. In this paper, we present a novel approach of directly learning deep embeddings for brain tumor types, which can be used for downstream tasks such as classification. Along with using triplet loss variants, our approach applies contrastive learning to performing unsupervised pre-training, combined with a rare-case data augmentation module to effectively ameliorate the lack of data problem in the brain tumor imaging analysis domain. We evaluate our method on an extensive brain tumor dataset which consists of 27 different tumor classes, out of which 13 are defined as rare. With a common encoder during all the experiments, we compare our approach with a baseline classification-layer based model, and the results well prove the effectiveness of our approach across all measured metrics.

Brain tumor represents one of the most fatal cancers around the world. It is common cancer in adults and children. It has the lowest survival rate and various types depending on their location, texture, and shape. The wrong classification of the tumor brain will lead to bad consequences. Consequently, identifying the correct type and grade of tumor in the early stages has an important role to choose a precise treatment plan. Examining the magnetic resonance imaging (MRI) images of the patient’s brain represents an effective technique to distinguish brain tumors. Due to the big amounts of data and the various brain tumor types, the manual technique becomes time-consuming and can lead to human errors. Therefore, an automated computer assisted diagnosis (CAD) system is required. The recent evolution in image classification techniques has shown great progress especially the deep convolution neural networks (CNNs) which have succeeded in this area. In this regard, we exploited CNN for the problem of brain tumor classification. We suggested a new model, which contains various layers in the aim to classify MRI brain tumor. The proposed model is experimentally evaluated on three datasets. Experimental results affirm that the suggested approach provides a convincing performance compared to existing methods.

Tumors of brain tissue and meninges create a heterogeneous group with various biological behavior, therapy management and differing prognosis. Some of these do not require treatment, some can be cured by surgery and some are rapidly fatal despite treatment. Despite huge progress in tumor research, innovations in diagnostic tools and therapy, prognosis remains, in case of malignant tumor types, very serious. There has been an increased understanding of molecular abnormalities occurring in primary brain tumors. Genome-wide analyses of tumors have improved the knowledge in tumor biology. The aim of the research is to explain the oncogenesis features thus leading to the use of new therapeutic modalities in order to prolong survival rate of patients and at the same time providing satisfactory life quality. This article offers a short review of the basic genetic alterations present with some histological types of brain tumors.