  Traditionally, diagnosis and treatment of fungal infections in humans depend
heavily on face-to-face consultations or examinations made by specialized
laboratory scientists known as mycologists. In many cases, such as the recent
mucormycosis spread in the COVID-19 pandemic, an initial treatment can be
safely suggested to the patient during the earliest stage of the mycological
diagnostic process by performing a direct examination of biopsies or samples
through a microscope. Computer-aided diagnosis systems using deep learning
models have been trained and used for the late mycological diagnostic stages.
However, there are no reference literature works made for the early stages. A
mycological laboratory in Colombia donated the images used for the development
of this research work. They were manually labelled into five classes and
curated with a subject matter expert assistance. The images were later cropped
and patched with automated code routines to produce the final dataset. This
paper presents experimental results classifying five fungi types using two
different deep learning approaches and three different convolutional neural
network models, VGG16, Inception V3, and ResNet50. The first approach
benchmarks the classification performance for the models trained from scratch,
while the second approach benchmarks the classification performance using
pre-trained models based on the ImageNet dataset. Using k-fold cross-validation
testing on the 5-class dataset, the best performing model trained from scratch
was Inception V3, reporting 73.2% accuracy. Also, the best performing model
using transfer learning was VGG16 reporting 85.04%. The statistics provided by
the two approaches create an initial point of reference to encourage future
research works to improve classification performance. Furthermore, the dataset
built is published in Kaggle and GitHub to foster future research.
