Grading of discs is essential for the assessment of degeneration progression which subsequently plays a vital role in decision making in the removal of a disc. In particular, Pfirrmann’s five-scale (1–5) scoring system is widely used in MR image modality for grading the discs. In this study, we have presented a contemporary semiautomatic feature level fusion approach for the classification of inter-vertebral discs. The data of T2-weighted lumbar MR scans in sagittal plane were collected from 120 distinct subjects. In total, 1123 inter-vertebral disc images were obtained upon performing image augmentation. The experts have segregated the discs into five categories as per Pfirrmann’s criteria. This segregation is utilized as ground truth label data for classification. Furthermore, two feature extraction techniques are exploited, one from spatial domain and other follows deep learning process. A popular Local Binary Pattern (LBP) texture descriptor extracts features from spatial domain. In addition, a popular pre-trained Convolution Neural Network (CNN), which acts as a feature extractor, extracts deep features. The training procedure using SVM classifier yields a model built from post-fusion feature vectors. Furthermore, to estimate the model’s performance, a 5-fold cross-validation is performed by computing principal component analysis as well as without dimensionality reduction. Experiment results obtained on our dataset indicate that after dimensionality reduction, SVM classifier with various kernel functions yields the accuracy up to 92%. A quantitative analysis of the classifier model is presented for parameters, namely – Accuracy, Area Under Curve (AUC), Specificity, Sensitivity, and F1 measure.

椎间盘分级对于评估退变进展至关重要，而退变进程随后在椎间盘切除决策中起着至关重要的作用。特别是，Pfirrmann 的五级 (1-5) 评分系统广泛用于 MR 图像模态，用于对椎间盘进行分级。在这项研究中，我们提出了一种用于椎间盘分类的现代半自动特征级融合方法。从 120 名不同的受试者中收集了矢状面 T2 加权腰椎 MR 扫描数据。在执行图像增强时，总共获得了 1123 个椎间盘图像。专家们根据 Pfirrmann 的标准将光盘分为五类。这种分离被用作用于分类的地面真值标签数据。此外，还利用了两种特征提取技术，一个来自空间域，另一个遵循深度学习过程。一种流行的局部二进制模式 (LBP) 纹理描述符从空间域中提取特征。此外，一种流行的预训练卷积神经网络 (CNN) 作为特征提取器提取深层特征。使用 SVM 分类器的训练过程会产生一个由融合后特征向量构建的模型。此外，为了估计模型的性能，通过计算主成分分析以及没有降维来执行 5 折交叉验证。在我们的数据集上获得的实验结果表明，在降维之后，具有各种核函数的 SVM 分类器的准确率高达 92%。对分类器模型的参数进行了定量分析，即准确度、曲线下面积 (AUC)、