AI-Based Detection and Classification of Spondylitis Using Deep Neural Networks

Abstract

Spondylitis is a spinal inflammatory disorder that can lead to chronic pain, stiffness, and reduced mobility if not diagnosed at an early stage. Traditional diagnosis methods rely heavily on manual examination of medical images, which can be time-consuming and prone to human error. To overcome these limitations, this project proposes an AI-based system for the detection and classification of spondylitis using Deep Neural Networks (DNNs). The proposed system uses medical imaging data such as X-ray or MRI scans to automatically identify the presence and type of spondylitis. Image preprocessing techniques are applied to enhance image quality and remove noise. A deep neural network model is then trained to extract important features from the images and classify them into normal or spondylitis-affected categories, and further into specific spondylitis types where applicable. The model improves diagnostic accuracy by learning complex patterns that are difficult to detect through traditional methods. Experimental results show that the deep learning-based approach achieves high accuracy and reliability in detecting spondylitis, reducing dependency on manual analysis. This system can assist healthcare professionals in early diagnosis, faster decision-making, and improved patient treatment outcomes. The proposed AI-based solution demonstrates the potential of deep learning techniques in medical image analysis and automated disease diagnosis.