A method for detecting and classifying the tumor regions in brain MRI images using vector index filtering and ANFIS classification process

Abstract

In this paper, the tumor affected images are detected and classified from non‐tumor affected brain magnetic resonance imaging (MRI) using Adaptive Neuro Fuzzy Inference System (ANFIS) classification process. The proposed work for brain tumor detection consists of a noise reduction module, decomposition module, feature extraction module, classification module, and segmentation module. The noisy brain images are filtered by the proposed Vector Index Filtering algorithm and the filtered images are further decomposed using Fast Wavelet Transform. Later, the index features are computed from each decomposed sub‐band image and these computed index features are classified either as normal or abnormal brain images using the ANFIS classification process. Further, the tumor regions are segmented in classified abnormal brain images using morphological functions. This paper uses the Brain Tumor Image Segmentation Challenge—BRATS 2016 dataset for analyzing the performance of the proposed brain tumor detection and segmentation system in terms of sensitivity (97.9%), specificity (98.6%), accuracy (99.1%), error rate (0.09%), F1‐score (96.8%), and Geometrics mean (98.5%). Comparisons are made between the proposed method's experimental results and other state of the art methods. The proposed methods using machine learning approach, stated in this paper are well suited for detecting tumor regions in brain images.

Study on the Annual Runoff Forecast Model of the Main Stream of Nanxi River Based on PSO-ANFIS

“…This algorithm solves the shortcomings of artificial neural network to a certain extent. Since it was proposed, it has been widely used in [21], classification [22,23], prediction [24], etc. In order to improve the calculation speed and accuracy of the model, some scholars have proposed to use the global optimization ability of PSO to optimize the structural parameters of ANFIS.…”