Early Bacterial Detection in Bloodstream Infection using Deep Transfer Learning Algorithm

An infection caused by bacteria can lead to severe complications affecting bloodstream disease. At present, blood cultures are used to identify bac-teria. However, blood culture is a time-consuming and labor-intensive method of diagnosing disease. The effect of delayed early diagnosis is that it influences the mortality risk. Thus, it is urgent to develop an initial prediction model to identify patients with bloodstream infections. This paper focused on classifying the bacteria using a deep-learning approach. Besides, deep learning techniques can enhance the bacterial classification process more effectively. The transfer learning-based convolutional neural network technique is used to develop our model. In addition, we compared the proposed model with another model used to find the best results. Compared to other models, the proposed model achieved an evaluation score with high accuracy of 98.62%. Medical decision-making may benefit from the proposed approach.