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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Babu Mudunuri, Suresh
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Computer Science & IT Engineering

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Design of a novel deep network model for spinal cord injury prediction Venkatapathi Raju, P. R. S. S.; Asanambigai, Valayapathy; Babu Mudunuri, Suresh
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp2131-2142

Abstract

Degenerative cervical myelopathy must be diagnosed with magnetic resonance imaging (MRI) which predicts spinal cord injury (SCI). The growing volume of medical imaging data can be managed by deep learning models, which provide a preliminary interpretation of images taken in basic care settings. Our main goal was to create a deep-learning approach that could identify SCI using MRI data. This work concentrates on modeling a novel 2D-convolutional neural networks (2D-CNN) approach for predicting SCI. For holdouts, training, and validation, various datasets of patients were created. Two experts assigned labels to the images. The holdout dataset was used to evaluate the performance of our deep convolutional neural network (DCNN) over the image data from the available dataset. The dataset is acquired from the online resource for training and validation purpose. With the available dataset, the anticipated model attains 94% AUC, 0.1 p-value, and 92.2% accuracy. The anticipated model might make cervical spine MRI scan interpretation more accurate and reliable.
Co-Authors Asanambigai, Valayapathy Venkatapathi Raju, P. R. S. S.