Brain Tumor Detection and Classification with Feed Forward Back-Prop Neural Network

TL;DR

This paper presents an automated brain tumor detection and classification method using MRI images, combining statistical, morphological, and thresholding techniques with a feed-forward back-propagation neural network to achieve high accuracy and efficiency.

Contribution

It introduces a novel combination of image processing techniques with neural networks for faster and more accurate brain tumor classification.

Findings

High accuracy in tumor detection

Reduced detection time due to fewer iterations

Effective classification of tumor types

Abstract

Brain is an organ that controls activities of all the parts of the body. Recognition of automated brain tumor in Magnetic resonance imaging (MRI) is a difficult task due to complexity of size and location variability. This automatic method detects all the type of cancer present in the body. Previous methods for tumor are time consuming and less accurate. In the present work, statistical analysis morphological and thresholding techniques are used to process the images obtained by MRI. Feed-forward back-prop neural network is used to classify the performance of tumors part of the image. This method results high accuracy and less iterations detection which further reduces the consumption time.