Gram staining of intestinal flora classification based on convolutional neural network

Gram staining is a traditional bacteriological laboratory technique, which has widely usage on many medical research and application. However, gram staining reading is a time consumption work. In this paper, we employ Convolutional Neural Network method to design a classifier, by which gram staining images can be identified as normal group and disease model group effectively and correctly. And image generate method is used to improve classification accuracy. The fecal gram stain smears of health rats and irritable bowel syndrome model rats are employed to validate the proposed model. The experiment results shown that our method can reach a validation accuracy of 95.6%. The method proposed in this paper can provide a quick and objective judgement for medical researchers or clinical diagnosis.

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