The number of persons suffering from spinal tumor has increased significantly from 2010 to 2016. Tumor is one of the major diseases of spinal cord. Thousands of researchers have concentrated on this disease to provide more efficient diagnosis with better understanding of the classification of spinal cord tumor. The proposed convolutional neural network (CNN) is tested with two hybrid recognized techniques of image detection which are K–nearest neighbor (KNN) with principal component analysis (PCA), local binary patterns (LBP) with support vector machine (SVM). Above three techniques overall accuracy is demonstrated, which show that LBP with SVM gives better result compared to KNN with PCA. The proposed CNN provides high accuracy classification and detection of spine diseases compared to other three techniques, which have obtained a best detection accuracy of 99.41 %. This process is fully implemented in MATLAB tool.

中文翻译：

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MRI脊柱图像分类的混合技术

从2010年到2016年，患脊柱肿瘤的人数显着增加。肿瘤是脊髓的主要疾病之一。成千上万的研究人员专注于这种疾病，以提供更有效的诊断并更好地了解脊髓肿瘤的分类。所提出的卷积神经网络（CNN）已通过两种混合识别的图像检测技术进行了测试，它们是具有主成分分析（PCA）的K近邻（KNN），具有支持向量机（SVM）的局部二进制模式（LBP）。以上三种技术的整体准确性得到了证明，表明与支持PCA的KNN相比，支持SVM的LBP效果更好。与其他三种技术相比，拟议的CNN可以对脊椎疾病进行高精度分类和检测，最佳检测精度为99.41％。此过程已在MATLAB工具中完全实现。