DeFungi: Direct Mycological Examination of Microscopic Fungi Images

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 using transfer learning was VGG16 reporting 85.04 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.


page 4

page 5

page 6


Convolutional Neural Networks for Multi-class Histopathology Image Classification

There is a strong need for automated systems to improve diagnostic quali...

A Deep Learning Study on Osteosarcoma Detection from Histological Images

In the U.S, 5-10% of new pediatric cases of cancer are primary bone tumo...

Automatic Detection of Coronavirus Disease (COVID-19) Using X-ray Images and Deep Convolutional Neural Networks

The 2019 novel coronavirus (COVID-19), with a starting point in China, h...

Automated diagnosis of COVID-19 with limited posteroanterior chest X-ray images using fine-tuned deep neural networks

The novel coronavirus 2019 (COVID-19) is a respiratory syndrome that res...

Novel Fundus Image Preprocessing for Retcam Images to Improve Deep Learning Classification of Retinopathy of Prematurity

Retinopathy of Prematurity (ROP) is a potentially blinding eye disorder ...

Semantic Segmentation of Histopathological Slides for the Classification of Cutaneous Lymphoma and Eczema

Mycosis fungoides (MF) is a rare, potentially life threatening skin dise...

Please sign up or login with your details

Forgot password? Click here to reset