Analysis and Classification of Abnormal Vertebral Column by Convolutional Neural Network Algorithm

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Witchuda Thongking
Pusit Mitsomwang
Bura Sindhupakorn
Jessada Tathanuch

Abstract

       This research applied the convolutional neural network (CNN algorithm) to determine the misalignment of vertebral column from the processed image. The raw data was the 3D-computerized tomography (CT) provided by the Suranaree University of Technology Hospital, Nakhon Ratchasima, Thailand. There were 93 data sets that comprised 40 data of misalignment vertebral columns. These studies first extracted front, rear, left, and right images of the vertebral column from 3D CT images by RadiAnt Program (Version 2020.2). In the second step, the images were processed by the Ridge detection algorithm with various parameters. The combinations processed were of sigma 1, 4, 7, and 10 with the two low-high thresholds, 10-30 and 20-20. The last step was about the Python code development (with Tensorflow, Numpy, and Sklearn libraries) for creating the model to classify the normal and abnormal vertebral column image sets by the CNN algorithm. The best model could perform very well. The model with Ridge detection preprocessing of parameters sigma=7, low threshold=20, and high threshold=20 performed faultlessly. The performance was accuracy 100 percent, precision 100 percent, and recall 100 percent.

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