Year: 2024
Type: conference-paper
Abstract: The spine provides structural support for the body.Identifying spinal abnormalities is crucial for assessing spinal health.Deep learning has gained popularity due to better performance compared to the traditional machine learning approaches.This study investigates the performance of a deep learning model in classifying different types of spinal abnormalities, namely scoliosis, spondylosis, and lesions using x-ray images.The deep learning model is trained and evaluated using two public datasets.The images in the first dataset are labelled to three classes; scoliosis, spondylosis, and normal, while the images in the second dataset are labelled to normal and lesion.The model was trained and tested on four different classification tasks.The deep learning model achieved accuracy of 100% for two binary and one multiclass classification of images from the first dataset and 97.5% accuracy for binary classification of images from the second dataset.The classification model would be a beneficial tool for medical professionals to perform initial diagnosis.
DOI: 10.5109/7323381
Authors: Muhammad Shahrul Zaim bin Ahmad, Nor Azlina Ab. Aziz, Lim Heng Siong
Institution: Multimedia University
Language: English
Topic: Medical Imaging and Analysis
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4404653632