Human Fungal Infection Image Classification Based on Convolutional Neural Network

Abstract

An improved algorithm for deep learning of convolutional neural network is proposed in this paper to automatically extract feature of the fungal images. Firstly, the target image of the connected area is used to detect the targets of the fungal image, and several small images of conidia in the original image are obtained. Secondly, the small image is augmented by some operations, the augmented small images are proportionally divided into training sets and validation sets, and the training accuracy and validation accuracy are obtained. Finally, the test unknown images are input into the model, and the test accuracy is obtained. Experimental results show that the measures of data augmentation and fine-tuning not only effectively avoid the over-fitting of deep learning algorithm in small samples, but also improve the accuracy. The training accuracy of the algorithm can reach 95%, the validation accuracy can reach 96%, and the test accuracy can reach 69.23%, which has good robustness and generalization.