Classification of fungal genera from microscopic images using artificial intelligence

Abstract

Microscopic image examination is fundamental to clinical microbiology and often used as the first step to diagnose fungal infections. In this study, we present classification of pathogenic fungi from microscopic images using deep convolutional neural networks (CNN). We trained well-known CNN architectures such as DenseNet, Inception ResNet, InceptionV3, Xception, ResNet50, VGG16, and VGG19 to identify fungal species, and compared their performances. We collected 1079 images of 89 fungi genera and split our data into training, validation, and test datasets by 7:1:2 ratio. The DenseNet CNN model provided the best performance among other CNN architectures with overall accuracy of 65.35% for top 1 prediction and 75.19% accuracy for top 3 predictions for classification of 89 genera. The performance is further improved (>80%) after excluding rare genera with low sample occurrence and applying data augmentation techniques. For some particular fungal genera, we obtained 100% prediction accuracy. In summary, we present a deep learning approach that shows promising results in prediction of filamentous fungi identification from culture, which could be used to enhance diagnostic accuracy and decrease turnaround time to identification.

Keywords: Artificial intelligence; Convolutional neural network; Fungal genera classification; Mycology.

Fig. 1

Microscopic images of some prevalent genera.

Fig. 2

A general CNN architecture for fungal image classification.

Fig. 3

Distribution of 89 classes of fungal genera.

Fig. 4

Prediction accuracy of some prevalent genera.

Fig. 5

Test accuracy comparison between our study and previous study for some genera used in the previous study. Previous study results are for the model “AlexNet FV SVM” and “Densenet 169” in Zielinski et al.

Fig. 6

An example prediction result of our trained model (DenseNet) that shows image class, saliency map, and gradient class activation map (GradCAM).