Automated Scoliosis Detection Using Advanced Segmentation Techniques

Scoliosis, a spinal deformity characterized by an abnormal curvature of the spine, requires early detection and precise diagnosis for effective management and treatment.Traditional methods for scoliosis detection often rely on manual assessment of X-ray images or physical examinations, which can be time-consuming and subjective.This project presents a novel approach to scoliosis detection using machine learning (ML) techniques.By leveraging deep learning algorithms, particularly convolutional neural networks (CNNs), we propose an automated system that analyses spinal X-ray images to identify and classify scoliosis with high accuracy.The system is trained on a diverse dataset of labelled X-ray images, capturing a range of scoliosis severities and variations.Key features include the extraction of spinal curvature metrics and the automated classification of scoliosis severity levels.Our results demonstrate that the ML-based approach significantly enhances detection accuracy, reduces the need for manual interpretation, and provides a scalable solution for early scoliosis diagnosis.This approach not only streamlines the diagnostic process but also holds promise for improving patient outcomes through timely and precise intervention.