Publications

Automated Pulmonary Hypertension Detection Model Based on EfficientNetb0 and DenseNet201 Using CT Images

Tarik Kivrak, Jagadish Nayak, Mehmet Ali Gelen, Prabal Datta Barua, Mehmet Baygin, Hilal Erken Pamukcu

Pulmonary hypertension (PH) is a chronic and progressive disease. We introduced a novel automated self-organized feature engineering architecture for PH detection, which was trained and refined using a new thoracic CT image dataset.

Artificial intelligence for prediction of chronic thromboembolic pulmonary hypertension using electrocardiography

Gonenc Kocabay, Tarik Kivrak, Turker Tuncer, Sengul Dogan, mehmet ali kobat

With this study researchers achieved the most weighted chosen features. 98.05% success with the Decision Tree algorithm, 99.89% with the SVM algorithm, and 99.67% KNN algorithm. The proposed method has two significant contributions. The first contribution is the development of a classification method with a high success rate.