Environmental microorganism classification using optimized deep learning model

Rapid environmental microorganism (EM) classification under microscopic images would help considerably identify water quality. Because of the development of artificial intelligence, a deep convolutional neural network (CNN) has become a major solution for image classification. Three popular CNNs, referred to as ResNet50, Vgg16, and Inception-v3, were transferred to identify the EM images present on the Environmental Microorganism Dataset (EMDS), and EMAD was the small dataset, which only has 294 EM images with 21 EM classes. Besides data augmentation, optimizing the fully connected layer of CNN, i.e., both optimally fine-tuned neuron number and dropout rate, was adopted to enhance the performance produced by CNN. The discussions on the causes of the accuracy improved by optimization are also provided. The results showed that the Inception-v3 model obtained 84.9% of the accuracy and performed better than the other two famous CNNs. Also, the implement of data augmentation enhanced the performance of Inception-v3 on EMDS. To add to that, the optimized Inception-v3 model archived 90.5% of the accuracy, and this result demonstrated the improvement effect obtained by using genetic algorithm (GA) to optimize the fully connected layer of the Inception-v3. Therefore, the optimize Inception-v3 with data augmentation process obtained the accuracy of 92.9% and improved almost 21% higher than that obtained from the famous Vgg16. In addition, the optimized Inception-v3 would need less neurons, when compared with that of the optimized Vgg16 possibly. This optimized Inception-v3 could provide a solution to the EM classification in microscope with a digital camera system.