A Deep Learning Approach for Automatic Scoliosis Cobb Angle Identification
Renato R. Maaliw
a
,
Julie Ann B. Susa
a
,
Alvin S. Alon
b
,
Ace C. Lagman
c
,
Shaneth C. Ambat
c
,
Manuel B. Garcia
c
,
Keno C. Piad
d
,
Ma. Corazon G. Fernando
c
a College of Engineering Southern Luzon State University, Lucban, Quezon, Philippines
b Digital Transformation Center Batangas State University, Batangas City, Philippines
c Information Technology Dept., FEU Institute of Technology, Manila, Philippines
d Information Technology Dept., Bulacan State University, Malolos, Bulacan, Philippines
Abstract: Efficient and reliable medical image analysis is indispensable in modern healthcare settings. The conventional approaches in diagnostics and evaluations from a mere picture are complex. It often leads to subjectivity due to experts' various experiences and expertise. Using convolutional neural networks, we proposed an end-to-end pipeline for automatic Cobb angle measurement to pinpoint scoliosis severity. Our results show that the Residual U-Net architecture provides vertebrae average segmentation accuracy of 92.95% based on Dice and Jaccard similarity coefficients. Furthermore, a comparative benchmark between physician's measurement and our machine-driven approach produces an acceptable mean deviation of 1.57 degrees and a T-test p-value of 0.9028, indicating no significant difference. This study has the potential to help doctors in prompt scoliosis magnitude assessments.