Detection and Classification of Brain Tumor from MRI Medical Image using Wavelet Transform and PSO based LLRBFNN Algorithm - Gopi Krishna, T; Sunitha, KVN; Mishra, S (International Journal of Computer Sciences and Engineering, 2025)
It is a difficult and complex task for a radiologist or clinical practitioner to segment, detect, and extract infected tumor area and classify the type of tumor from magnetic resonance (MR) images. This paper presents a PSO (Particle Swarm Optimization) based LLRBFNN (Local Linear Radial Basis Function Neural Network) model to classify and detect brain tumors into malignant (cancerous) and benign (noncancerous). In this paper we have used wavelet transform to improve the performance of MR image segmentation process and feature extraction. For the validation of the proposed PSO based LLRBFNN model, the machine learning approach support vector machine (SVM) and LMS (Least Mean Square) based LLRBFNN classifier also investigated. The research work follows the steps such as feature extraction out of which relevant features are considered for the research work. In the second step the features are fed as input to the proposed PSO based LLRBFNN Model for the classification task. In the third step the machine learning approach SVM and LMS based LLRBFNN has been applied for classification task and the results are compared. It is found that the proposed model takes less computational time than the SVM and LMS based LLRBFNN machine learning approach. In contrast to classification results the proposed model gives better classification results. Based on accuracy it is also noticed that the proposed model shows better performance in accuracy and quality analysis on MRI brain images.