Abstract
Accurate detection of brain tumors could save lots of lives and increasing the accuracy of this binary classification even 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 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 and compared with traditional segmentation techniques. The proposed method is fast and precise in both tasks of tumor classification and segmentation compared with other methods. A classification accuracy of 95.14 % and segmentation accuracy of 0.977 is achieved by the proposed method.
Abstract (translated)
准确检测脑瘤可以挽救大量生命,而提高这种二分类的准确性即使只有几个百分点也非常重要。神经气体网络(NGN)是一种快速、无监督算法,可用于数据聚类、图像模式识别和图像分割。在本研究中,我们使用优化算法萤火虫算法(FA)进行图像对比度增强作为预处理,并将NGN权重用于从Kaggle平台上的两个脑瘤数据集中提取特征和分割MRI数据。此外,肿瘤分类由支持向量机(SVM)分类算法进行,并与深度学习技术和传统分割技术进行比较。此外,NGN肿瘤分割使用著名的性能指标,如准确率、F-measure、Jaccard和更多的与实际真相数据进行比较,并与传统分割技术进行比较。 proposed method在肿瘤分类和分割任务中的速度和精度都与其他方法相比非常迅速和精确。与其他方法相比,该方法实现了95.14%的分类准确率和0.977%的分割准确率。
URL
https://arxiv.org/abs/2301.12176
