The application of deep learning in the identification of Mycobacterium tuberculosis in acid-fast staining of sputum
Objective To establish a learning model that could accurately identify Mycobacterium tuberculosis(Mtb)in acid-fast stained sputum smears,and provide technical support for faster,more efficient and more accurate diagnosis of pulmonary tuberculosis.Methods The positive images of Mtb acid-fast staining in sputum were collected from the published literature of PubMed database and Tuberculosis Image Dataset.All images were normalized by directly dividing the pixel values by 255,and standardized using Batch Normalization.Faster R-CNN using transfer learning technology was used to construct a deep learning object detection model.The performance of the model was evaluated by calculating the model accuracy,intersection over union(IoU),and mean average precision(mAP),and drawing the loss-epochs curve.Results A total of 1 265 Mtb acid-fast staining images from the Tuberculosis Image Dataset and 60 Mtb acid-fast staining images from 33 literatures in PubMed database were collected for the construction of Faster R-CNN deep learning object detection model.The model showed good prediction effect.The loss-epochs curve was relatively smooth,showing an obvious downward trend,and tended to be stable at about 60 iterations.The accuracy,IoU and mAP of the training set were 0.948 9,0.897 5 and 0.854 3,respectively,and those of the validation set were 0.693 1,0.546 0 and 0.845 3,respectively.The testing set comprises a total of 60 Mtb-positive images;out of which,the model accurately predicted 49,with an accuracy of 81.7％.Conclusion The Faster R-CNN deep learning objective detection model had a good ability to identify and classify acid-fast staining positive Mtb in sputum,which could be used as an auxiliary tool for clinical diagnosis of pulmonary tuberculosis.
Deep learningMycobacterium tuberculosisFaster R-CNN
万方数据
维普
