TaylorCSROA-based Deep Residual Network: An Optimization driven Deep Network for the multilevel spinal cord disease classification

Abstract

ABSTRACT Spinal cord is a cylindrical shape located in Central Nervous System (CNS) and extended between the medulla oblongata and lumbar vertebrae. The main task in the classification of multilevel spinal cord disease in Computed Tomography (CT) is the accuracy. In this research, a Taylor Crow Search-Rider Optimization Algorithm (TaylorCSROA) is developed for accurate classification of spinal cord disease. The segmentation is done through the adaptive thresholding method. The Sparse Fuzzy C-Means clustering (Sparse FCM) algorithm is implemented for the localization of disc. The features, like connectivity, Local Optimal-Oriented Pattern (LOOP), statistical, image-level, Grid-based shape features, and tetrolet features are extracted. The spinal cord disease is classified into intervertebral disc (IVD) bulges, the cal sac compressing, bone marrow disease, central or foraminal stenosis, annular tears, scoliosis, end plate degeneration, endplate defects (modic type), facet connection, and ligamentum flavum hypertrophy, and spondylolisthes is by training the Deep Residual Network using the developed TaylorCSROA. The developed TaylorCSROA algorithm derived from the integration of Taylor Series, Crow Search Algorithm (CSA), and the Rider Optimization Algorithm (ROA). When compared with the existing spinal cord disease classification methods, the developed method obtained a maximum accuracy of 0.9102, sensitivity of 0.8945, and specificity of 0.9245.