Neural Gas Network Image Features and Segmentation for Brain Tumor Detection Using Magnetic Resonance Imaging Data

Accurate detection of brain tumors could save lots of lives, and increasing the accuracy of this binary classification, even by as much as a few percent, has high importance. Neural Gas Networks (NGN) is a fast, unsupervised algorithm that could be used in data clustering, image pattern recognition, and image segmentation. In this research, we used the metaheuristic Firefly Algorithm (FA) for image contrast enhancement as pre-processing and NGN weights for feature extraction and segmentation of Magnetic Resonance Imaging (MRI) data on two brain tumor datasets from the Kaggle platform. Also, tumor classification is conducted by Support Vector Machine (SVM) classification algorithms and compared with a deep learning technique, plus other features in the train and test phases. Additionally, NGN tumor segmentation is evaluated by famous performance metrics such as accuracy, F-measure, Jaccard, and more versus ground truth data, as well as by comparing it with traditional segmentation techniques. The proposed method is faster and more precise in both tasks of tumor classification and segmentation than other methods. A classification accuracy of ۹۵.۱۴% and segmentation accuracy of ۰.۹۷۷ are achieved by the proposed method.

کلیدواژه ها:

Brain Tumor Detection ، Neural Gas Networks ، MRI ، SVM ، Firefly Algorithm ، Classification ، Segmentation