Pulmonary hypertension classification based on machine learning using standart chest x-ray : ata artificial intelligence study-1

Abstract

Amaç: An accurate diagnosis of pulmonary hypertension (PH) is crucial to ensure that patients receive timely treatment. One of the used imaging models to detect pulmonary hypertension is the X-ray. Therefore, a new automated PH type classification model has been presented to depict the separation ability of deep learning for PH types Yöntem: We retrospectively enrolled 6642 images of patients with PH and the control group. A new X-ray image dataset was collected from a multicenter in this work.A transfer learning-based image classification model has been presented in classifying PH types. Bulgular: Our proposed model was applied to the collected dataset, and this dataset contains six categories (five PH and a nonPH). The presented deep feature engineering (computer vision) model attained 86.14% accuracy on this dataset. According to the extracted ROC curve, the average area under the curve rate has been calculated at 0.945.