sipooioid / bacterial-atlas-identification-model-based-on-mbaa-cnn

For the spectral information of bacteria, the multi-branch attention mechanism of the tensorflow architecture and the convolutional neural network training model are used to distinguish bacterial types.

中文 / English

1.Implementation process

1.1Architectural model

1.1.1Convolutional neural network algorithm based on multi-branch attention improvement (MBAA-CNN)

Method	UNLABEL	LABEL
SVM	0.6981	0.7020
RNN	0.8078	0.8104
KNN	0.8567	0.8574
CNN	0.9097	0.9187
MBAA-CNN	0.9897	0.9909

2. Deployment method suggestions

• Training

python main.py

• Deployment

python Model_Apply.py

• Provide preprocessed Ten bacterial Raman spectrum test files for testing, and our pretrained Classification model . When using it, please extract the test file to the Final_Data folder and modify the file path in Model_Apply.py. For original data labels, you can set up a dictionary yourself, for example:

['Cns', 'E. cloacae', 'E. coli', 'K. pneumoniae', 'MC', 'MRSA', 'MSSA', 'P. aeruginosa', 'P. vulgaris', 'S. epidermidi']

• It is recommended to use a Linux environment to create a virtual environment for training

Please indicate the source for use!

References:

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