An Approach to Classifying X-Ray Images of Scoliosis and Spondylolisthesis Based on Fine-Tuned Xception Model - Lu, Quy Thanh; Nguyen, Triet Minh (International journal of advanced computer science & applications, 2024)
The vertebral column is a marvel of biological engineering and it considers a main part of the skeleton in vertebrate animals. In addition, it serves as the central axis of the human body comprising a series of interlocking vertebrae that provide structural support and flexibility. From basic works like bending and twisting to more complex actions such as walking and running, the spine's impact on human life is profound, underscoring its indispensable role in maintaining physical well-being and overall functionality. Moreover, in the hard-working schedule of people in modern life, a bunch of diseases impact on vertebral column such as spondylolisthesis and scoliosis. As a result, numerous researches were provided to take a hand in solving or avoiding these illnesses including machine learning. In this study, transfer learning and fine tuning were used for the classification of X-ray images on vertebrae sickness to avoid complex and wasted time in a medical examination process. The dataset for vertebrae illnesses X-ray images was collected at King Abdullah University Hospital and Jordan University of Science and Technology in Irbid, Jordan. It comprised 338 subjects including: 79 spondylolisthesis, 188 scoliosis, and 71 normal X-ray images. With the customized layers model in Xception that is used for image classification, we received surprisingly high results including validation accuracy, test accuracy, and F1 score in three-class classifications (i.e., spondylolisthesis, scoliosis, and normal) at 99.00%, 97.86%, and 97.86%, respectively. Additionally, two-class detection also received high accuracy values (i.e., 98.86% and 99.57%). Considering various high-performance metrics in the result indicates a robust ability to identify vertebrae diseases using X-ray images. The study found that machine learning significantly raises medical examinations compared to traditional methods, offering a myriad of benefits in terms of accuracy, efficiency, and diagnostic capabilities.