Artificial Neural Network Fuzzy Inference System (ANFIS) For Brain Tumor Detection

TL;DR

This paper proposes using an ANFIS model for brain tumor detection, demonstrating its superior classification accuracy over FCM and K-NN methods in medical image analysis.

Contribution

It introduces a novel application of ANFIS for brain tumor classification and compares its performance with existing fuzzy and nearest neighbor techniques.

Findings

ANFIS outperforms FCM and K-NN in classification accuracy.

The method effectively combines neural networks and fuzzy logic.

Experimental results show promising potential for clinical application.

Abstract

Detection and segmentation of Brain tumor is very important because it provides anatomical information of normal and abnormal tissues which helps in treatment planning and patient follow-up. There are number of techniques for image segmentation. Proposed research work uses ANFIS (Artificial Neural Network Fuzzy Inference System) for image classification and then compares the results with FCM (Fuzzy C means) and K-NN (K-nearest neighbor). ANFIS includes benefits of both ANN and the fuzzy logic systems. A comprehensive feature set and fuzzy rules are selected to classify an abnormal image to the corresponding tumor type. Experimental results illustrate promising results in terms of classification accuracy. A comparative analysis is performed with the FCM and K-NN to show the superior nature of ANFIS systems.