An auto contrast custom convolutional neural network to identifying Gram-negative bacteria

The process of identifying bacteria is an essential factor in the medical field. One of the germs that cause lung damage in pneumonia is gram-negative bacteria. The convolutional neural network method is the newest approach to machine learning because it has a high degree of accuracy. But the drawback is due to his in-depth knowledge, the computation time for the training process takes a long time. The method offered in this research is automatic contrast addition in the preprocessing stage and the use of custom layers. Also, augmentation data added to increase the variation in the amount of data in the training process. In using custom layers, the objective is to obtain minimal computational training time while maintaining maximum accuracy values. The results show that an average accuracy around 98.59% with average training time around 01 minutes 56 seconds, average MSE 0.0274, RMSE 0.1693, and MAE 0.0185.