An Improved Medical Text Classification Model: LS-GRU

In order to help radiologists report the CT image results more accurately and effectively to the clinicians, an improved GRU deep learning framework LS-GRU was proposed to solve the classification of image report text, which can be automatically fed back to clinicians according to radiologists' descriptions. The data was collected from more than 1 168 cases of respiratory imaging reports. Two diseases(emphysema and pneumonia) with similar descriptions of radiologists were classified. About 652 cases of emphysema and 516 cases of pneumonia were reported. The GRU, BiGRU and LSTM models were validated, respectively. The results show that the LS-GRU model is more accurate and robust. © 2020, Editorial Department of Journal of Northeastern University. All right reserved.