论文标题
使用特征提取和非线性SVM进行脑肿瘤分类的混合模型
Hybrid Model using Feature Extraction and Non-linear SVM for Brain Tumor Classification
论文作者
论文摘要
从磁共振成像（MRI）中对脑肿瘤进行分类至关重要，以便对患者进行更好和及时的治疗。在本文中，我们提出了一种混合模型，并使用VGG与非线性-SVM（软和硬）一起对脑肿瘤进行分类：神经胶质瘤和垂体，肿瘤和无肿瘤。 VGG-SVM模型已针对两个类别的两个不同数据集训练。因此，我们执行二进制分类。通过Pytorch Python文库对VGG模型进行了训练，以获得肿瘤分类的最高测试准确性。该方法为三倍，在第一步中，我们对图像进行了归一化和调整大小，第二步由通过VGG模型的变体提取特征提取。第三步使用非线性SVM（软和硬）将脑肿瘤分类。使用VGG19，我们已经获得了第一个数据集的98.18％精度，第二个数据集的精度为99.78％。非线性SVM的分类精度为95.50％和97.98％，线性和RBF内核的分类精度为97.95％，柔软的SVM为97.95％，带有D1的RBF内核，以及96.75％和98.60％的线性和98.60％，使用线性和RBF内核，以及98.38％的柔软SVM Kernel，RBF Kernel，RBF Kernel cornel cornel cornel cornel cornel cornel cornel cornel cornel cornel cornel d2 d2。结果表明，混合VGG-SVM模型，尤其是具有SVM的VGG 19，能够超越现有技术并实现高精度。
It is essential to classify brain tumors from magnetic resonance imaging (MRI) accurately for better and timely treatment of the patients. In this paper, we propose a hybrid model, using VGG along with Nonlinear-SVM (Soft and Hard) to classify the brain tumors: glioma and pituitary and tumorous and non-tumorous. The VGG-SVM model is trained for two different datasets of two classes; thus, we perform binary classification. The VGG models are trained via the PyTorch python library to obtain the highest testing accuracy of tumor classification. The method is threefold, in the first step, we normalize and resize the images, and the second step consists of feature extraction through variants of the VGG model. The third step classified brain tumors using non-linear SVM (soft and hard). We have obtained 98.18% accuracy for the first dataset and 99.78% for the second dataset using VGG19. The classification accuracies for non-linear SVM are 95.50% and 97.98% with linear and rbf kernel and 97.95% for soft SVM with RBF kernel with D1, and 96.75% and 98.60% with linear and RBF kernel and 98.38% for soft SVM with RBF kernel with D2. Results indicate that the hybrid VGG-SVM model, especially VGG 19 with SVM, is able to outperform existing techniques and achieve high accuracy.
