Classification of Lenke Scoliosis using GLCM Feature Extraction and Support Vector Machine - Anna Nur Chamim; Hasimah Ali; Yessi Jusman; Mohd Imran Yusof; Prasaca Pigama Priyanindhita; Asy-Syifa Febya Ananta (Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA, 2026)
Lenke scoliosis is a spinal deformity that is classified into six types by the Lenke classification system. Traditionally, clinicians undertake classification based on manual visual examination of X-ray images, which is time-consuming, requires high skill and is subject to errors caused by human fatigue. To overcome these constraints, the current work presents an automated and reliable classification system to boost the efficiency and accuracy of diagnosis. The method is based on the application of the Grey Level Co-occurrence Matrix (GLCM) for the feature extraction and of a Support Vector Machine (SVM) classifier. The main contribution is the optimisation of SVM kernel functions (Quadratic, Cubic and Coarse Gaussian) using advanced pre-processing methods to achieve very good accuracy while preserving compute efficiency suitable for clinical applications. The approach combines picture pre-processing (grey scale conversion, resize, contrast improvement by adaptive histogram equalisation, segmentation, augmentation) and GLCM-based feature extraction and classification using multiple SVM kernels. The model's performance is evaluated based on accuracy, precision, recall, F1 Score, and execution time. The testing results demonstrate that the Quadratic SVM has the best classification accuracy of 92.26% with a processing time of 20.44 seconds, which outperforms the Cubic SVM (90.97%, 19.30 seconds) and the Coarse Gaussian SVM (60.64%, 21.70 seconds). The results show that the quadratic SVM has the optimum compromise between accuracy and processing efficiency. In conclusion, the proposed GLCM-SVM approach has tremendous potential to support doctors in the automatic categorisation of Lenke scoliosis, improving the accuracy and speed of diagnosis without requiring large computational resources. In future work, we will aim to expand the dataset and include additional features to further improve the model's resilience and generalisability.