DeFungi

Donated on 1/28/2023

DeFungi is a dataset for direct mycological examination of microscopic fungi images. The images are from superficial fungal infections caused by yeasts, moulds, or dermatophyte fungi. The images have been manually labelled into five classes and curated with a subject matter expert assistance. The images have been cropped with automated algorithms to produce the final dataset.

Dataset Characteristics

Image

Subject Area

Computer Science

Associated Tasks

Classification

Feature Type

Real

# Instances

9114

# Features

-

Dataset Information

For what purpose was the dataset created?

The dataset was created to develop a machine-learning algorithm for detecting and classifying Fungi images.

Who funded the creation of the dataset?

No funder.

What do the instances in this dataset represent?

Photos

Are there recommended data splits?

No

Does the dataset contain data that might be considered sensitive in any way?

No

Was there any data preprocessing performed?

Yes. The dataset has been pre-processed. All images have been cropped to the region of interest.

Has Missing Values?

No

Introductory Paper

P456 Defungi: direct mycological examination of microscopic fungi images

By C. Sopo, Farshid Hajati, S. Gheisari. 2021

Published in Medical Mycology

Dataset Files

FileSize
H6/H6_31a_3.jpg48.8 KB
H6/H6_31a_1.jpg46 KB
H5/H5_43a_1.jpg45.5 KB
H5/H5_43a_3.jpg45.2 KB
H5/H5_81a_1.jpg45 KB

0 to 5 of 9114

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1 citations
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Creators

Farshid Hajati

hajati@gmail.com

Camilo Javier Pineda Sopo

camilo.pinedasopo@live.vu.edu.au

Victoria University

Farshid Hajati

farshid.hajati@vu.edu.au

Victoria University

Soheila Gheisari

soheila.gheisari@vu.edu.au

Victoria University

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