Automatic Severity Classification of Coronary Artery Disease via Recurrent Capsule Network
TABLE V: Performance of Our Automatic Severity Classification Method
['[EMPTY]', 'P', 'R', 'F{}_{1}']
To evaluate the effectiveness of our proposed severity classification method, we randomly select 200 coronary arteriography texts for evaluation. First of all, our method obtains an overall Accuracy of 97.00%. Only six texts are classified into the wrong level. Secondly, most of the coronary arteriography texts (72.5%) belongs to mild stenosis in practice. Our method achieves an relative high performance in terms of Precision (100%), Recall (98.62%) and F{}_{1}-score (99.31%). Thirdly, our method is a little confused with moderate stenosis and severe stenosis. The precision, recall and F{}_{1}-score of moderate stenosis are all 93.33%. It is still acceptable. As to severe stenosis, which appears rarely (5%) in practice, though the precision is merely 75.00%, but the recall is 90%. That is to say, only one text of severe stenosis is not recognized by our method.