Spondylitis Detection Using Deep Learning Architecture

Abstract

Spondylitis is a chronic inflammatory disease that primarily affects the spine and can lead to severe pain, stiffness, and eventually spinal deformity. Traditional methods for diagnosing spondylitis rely heavily on radiographic interpretation by specialists, which can be time-consuming and subjective. With the growing availability of medical imaging data and advancements in computational capabilities, there is a rising interest in the automation of diagnostic processes through deep learning. This project presents a deep learning-based solution for the automated detection of spondylitis using spinal X-ray and MRI images. The proposed system employs advanced convolutional neural networks (CNNs) capable of learning hierarchical features from medical images. By training the model on labeled datasets, it can differentiate between healthy and affected spinal structures with high accuracy. The system is designed to reduce diagnostic delays and improve reliability by minimizing human error. In conclusion, this deep learning architecture provides a promising tool for early and accurate detection of spondylitis. It serves as a decision support system for radiologists and orthopedic specialists, streamlining the diagnostic workflow in clinical settings. Future enhancements may include multidisease classification, integration with hospital systems, and deployment in mobile health applications for remote diagnosis.