Brain Tumor Detection Using Neural Classification in Machine Learning

Abstract

Brain cancer classification is a difficult task due to the variety and complexity of tumors shown in magnetic resonance imaging (MRI) pictures. This research presents two neural network approaches for categorizing MRI brain images. The proposed neural network method consists of three steps: feature extraction, dimensionality reduction, and classification. First, we extracted features from MRI images using discrete wavelet transformation (DWT). In this second stage, we reduce the salient features of MRIs using principal component analysis (PCA). For the classification step, two supervised machine learning classifiers have been developed. Artificial neural networks are used by both classifiers; however, the second one employs back propagation (BPN) while the first one uses feed-forward (FF-ANN). Using the classifiers, MRI brain images of the subjects were classified as normal or abnormal. Artificial neural networks have numerous applications, including function approximation, feature extraction, optimization, and classification (ANNs). They are specifically intended to enhance photos, distinguish and categorize items, separate and register objects, and extract features. Among these, object and picture recognition is the most important for complex processing tasks such as classifying brain tumors. Radial basis function (RBF), cellular, multi-layer perceptron (MLP), hop field, and pulse-coupled neural networks have all been used in image segmentation. These networks can be categorized as feed-forward (associative) or feedback (auto-associative).