A DEEP LEARNING-BASED APPROACH FOR THE DIAGNOSTICOF BRUCELLAR SPONDYLITIS IN MAGNETIC RESONANCE IMAGES

Abstract

Spondylitis is a chronic inflammatory condition affecting the spine, often leading to pain, stiffness, and reduced mobility if not diagnosed at an early stage. Conventional diagnostic methods rely heavily on clinical evaluation and imaging techniques such as X-rays, MRI, and CT scans, which require expert interpretation and may delay timely detection. To address these challenges, this study proposes a deep learning-based architecture for the automated detection of spondylitis from medical imaging data. The proposed system utilizes convolutional neural networks (CNNs) to analyze spinal images and extract relevant features associated with spondylitis. Preprocessing techniques such as image normalization, noise reduction, and augmentation are applied to enhance data quality and improve model generalization. The architecture may incorporate pre-trained models such as ResNet, VGG, or Efficient Net through transfer learning to achieve higher accuracy with limited datasets. The model is trained and

Validated using labeled medical datasets to classify images into normal and spondylitis - affected categories. Experimental results demonstrate that the deep learning- based approach significantly improves diagnostic accuracy, sensitivity, and specificity compared to traditional methods. The system provides a fast, reliable, and automated solution for early detection, assisting health care professionals in clinical decision-making.Furthermore,the proposed model aims to reduce human error, optimize diagnostic time, and enhance patient outcomes, while ensuring scalability and adaptability in real-world medical applications.