@Article{cmc.2020.013125,
AUTHOR = {Chung Le Van, Vikram Puri, Nguyen Thanh Thao, Dac-Nhuong Le},
TITLE = {Detecting Lumbar Implant and Diagnosing Scoliosis from Vietnamese X-Ray Imaging Using the Pre-Trained API Models and Transfer Learning},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {66},
YEAR = {2021},
NUMBER = {1},
PAGES = {17--33},
URL = {http://www.techscience.com/cmc/v66n1/40429},
ISSN = {1546-2226},
ABSTRACT = {With the rapid growth of the autonomous system, deep learning has
become integral parts to enumerate applications especially in the case of healthcare systems. Human body vertebrae are the longest and complex parts of the
human body. There are numerous kinds of conditions such as scoliosis, vertebra
degeneration, and vertebrate disc spacing that are related to the human body vertebrae or spine or backbone. Early detection of these problems is very important
otherwise patients will suffer from a disease for a lifetime. In this proposed system, we developed an autonomous system that detects lumbar implants and diagnoses scoliosis from the modified Vietnamese x-ray imaging. We applied two
different approaches including pre-trained APIs and transfer learning with their
pre-trained models due to the unavailability of sufficient x-ray medical imaging.
The results show that transfer learning is suitable for the modified Vietnamese
x-ray imaging data as compared to the pre-trained API models. Moreover, we also
explored and analyzed four transfer learning models and two pre-trained API
models with our datasets in terms of accuracy, sensitivity, and specificity.},
DOI = {10.32604/cmc.2020.013125}
}