Detecting Lumbar Implant and Diagnosing Scoliosis from Vietnamese X-Ray Imaging Using the Pre-Trained API Models and Transfer Learning

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.

Transfer Learning Approach for Classification of Histopathology Whole Slide Images

“…The suggested model was tested on a dataset of lung cancer by scientists, and the model reported accuracy of 0.97. Another study [ 43 ] suggested three different CNN models to classify the coronavirus contamination in X-radiation cases, including Inception-ResNetV2, InceptionV3, and ResNet50. In terms of detection and identification, the ResNet50 system outperformed InceptionV3 and Inception-ResNetV2 with 0.98 accuracy, whereas InceptionV3 attained 0.97, and Inception-ResNetV2 attained 0.87.…”