Identification of environmental microorganism using optimally fine-tuned convolutional neural network

To not only optimize the hyper-parameters of the classification layer of dense convolutional network with 201 convolutional layers (DenseNet-201) but also use data augmentation processes could enhance the performance of DenseNet-201, and DenseNet-201 is rarely applied to the identifications of the environmental microorganism (EM) images. Hence, this study was to propose the optimally fine-tuned DenseNet-201 (OFTD) with data augmentation to better classify the EM images on Environmental Microorganism Dataset (EMDS). The training dataset was composed of 70% Environmental Microorganism Dataset (EMDS) images and so was mainly used to fit the parameters of convolutional layers of optimally fine-tuned DenseNet-201 (OFTD). Meanwhile, the other EMDS images were considered as the testing dataset and used to qualify the performance of the OFTD. Also, gradient-weighted class activation mapping method (Grad-CAM) was adopted to visually illustrate the dominant features of the EM images. Based on the results, the OFTD model with data augmentation achieved the highest classification accuracy of 98.4%. In this case, so its stability and accuracy were guaranteed. Besides, the optimally fine-tuned classification layer is considered a more efficient method than the data augmentation technique adopted in this study when it comes to the improvement of the performance in DenseNet-201 implemented on EMDS. Grad-CAM highlighted the coarse EM features identified effectively by the OFTD; for example, foot and stalk were considered as the dominated features of Rotifera and Vorticella, respectively. In summary, the proposed OFTD with data augmentation could provide an efficient solution for the EM detection in digital microscope.