Brain tumor Classification using Optimized and Relief-based Feature Reduction and Regression Neural Network

Abstract

The brain is an essential part of the human body for carrying out normal functions. Nowadays, a person's fast-paced lifestyle combined with an unhealthy diet can lead to brain damage. The current state of medicine and imaging could prove to be a great boon in this case. The image's restricted borders and lack of textural variety make it difficult to detect the tumour even in its earliest stages. Before, a picture was categorised with the help of a hybrid feature extraction system and an Extreme Learning Machine. Science also makes use of projection-based categorization. In this study, we will use MRI image characteristics to categorize different types of brain cancer. This is how a neural network is constructed based on patterns for fast processing. An anisotropic diffusion filter was used in the preprocessing of these images. The cancerous tissue is then separated using thresholding and a morphological strategy. Based on the local binary patterns and histogram-oriented gradients of cancer areas, the pattern-oriented features were generated. What we care about are the global, first-order properties of the image. In order to better all learned characteristics, the error rate and the method of relief are both decreased. A regression neural network is then used to categories the feature with the highest ratings from both PSO and the relief technique. All of the work is done in MATLAB R2020b on Windows 10 with the fig-sharing tumour dataset. Finally, its accuracy, sensitivity, and specificity are compared to those of alternative methods.