Brain tumor classification is a crucial task to evaluate the tumors and make a treatment decision according to their classes. There are many imaging techniques used to detect brain tumors. However, MRI is commonly used due to its superior image quality and the fact of relying on no ionizing radiation. Deep learning (DL) is a subfield of machine learning and recently showed a remarkable performance, especially in classification and segmentation problems. In this paper, a DL model based on a convolutional neural network is proposed to classify different brain tumor types using two publicly available datasets. The former one classifies tumors into (meningioma, glioma, and pituitary tumor). The other one differentiates between the three glioma grades (Grade II, Grade III, and Grade IV). The datasets include 233 and 73 patients with a total of 3064 and 516 images on T1-weighted contrast-enhanced images for the first and second datasets, respectively. The proposed network structure achieves a significant performance with the best overall accuracy of 96.13% and 98.7%, respectively, for the two studies. The results indicate the ability of the model for brain tumor multi-classification purposes.

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Multi-Classification of Brain Tumor Images Using Deep Neural Network

An enhanced deep learning approach for brain cancer MRI images classification using residual networks

A Review on Brain Tumor Diagnosis from MRI Images : Practical Implications, Key Achievements, and Lessons Learned

Multiclass magnetic resonance imaging brain tumor classification using artificial intelligence paradigm.

Identification of brain tumors attends a critical role in evaluating tumors and making decisions about care as per their grades Several imaging methods are employed to identify brain tumors Though, leading to its excellent image quality and the reality that it depends on no cosmic radiation, MRI is widely utilized Deep learning (DL) is a computer vision field of study and has shown remarkable output currently, notably in classification and segmentation issues This article proposes, DL design based on a Convolution Neural Network (CNN) to identify various types of brain tumors leveraging two publicly accessible resources or databases The previous identify tumors into (Meningioma, Glioma, and Pituitary tumors) Another one distinguishes between all three categories (Grade II, Grade III, and Grade IV)

Programmed Multi-Classification of Brain Tumor Images Using Deep Neural Network

This paper proposes an intellectual classification system to recognize normal and abnormal MRI brain images. Nowadays, decision and treatment of brain tumors is based on symptoms and radiological appearance. Magnetic resonance imaging (MRI) is a most important controlled tool for the anatomical judgment of tumors in brain. In the present investigation, various techniques were used for the classification of brain cancer. Under these techniques, image preprocessing, image feature extraction and subsequent classification of brain cancer is successfully performed. When different machine learning techniques: Support Vector Machine (SVM), K- Nearest Neighbor (KNN) and Hybrid Classifier (SVM-KNN) is used to classify 50 images, it is observed from the results that the Hybrid classifier SVM-KNN demonstrated the highest classification accuracy rate of 98% among others. The main goal of this paper is to give an excellent outcome of MRI brain cancer classification rate using SVM-KNN.

Hari Babu Nandpuru

Papers

MRI brain cancer classification using hybrid classifier (SVM-KNN)