DOI: 10.5061/dryad.8931zcrw5
title: CCI.net fungal keratitis cases dataset

description: Patients diagnosed with FK were enrolled from three cities in China
 between April 2015 and June 2022. The diagnosis was confirmed either by
 corneal cultures or corneal biopsy. Patients who were over 65 years old,
 had systemic diseases such as diabetes, autoimmune diseases,
 thyroid-related diseases, or were pregnant, were excluded. Patients with
 bacterial, amebic, or other infections confirmed by culture were also
 excluded. A total of 355 patients were included, comprising 212 from
 Zhongshan Ophthalmic Center in Guangdong Province, 83 from Xi'an
 First Hospital in Shanxi Province, and 60 from Guangxi Zhuang Autonomous
 Region People's Hospital in Guangxi Zhuang Autonomous Region. There
 were 5 licensed ophthalmologists using standard processing to grade the
 image quality. A total of 8697 images were graded as good quality and were
 further divided into three datasets: a classification dataset (N=7086,
 including 4255 in the training set, 1413 in the validation set, and 1418
 in the test set), a segmentation dataset (N=56, including 46 in the
 training set and 10 in the test set), and an external validation set
 (N=1555, including 1455 to validate the classification model and 100 to
 validate the segmentation model).
description: Patients diagnosed with FK were enrolled from three cities in
 China between April 2015 and June 2022. The diagnosis was confirmed either
 by corneal cultures or corneal biopsy. Patients who were over 65 years
 old, had systemic diseases such as diabetes, autoimmune diseases,
 thyroid-related diseases, or were pregnant, were excluded. Patients with
 bacterial, amebic, or other infections confirmed by culture were also
 excluded. A total of 355 patients were included, comprising 212 from
 Zhongshan Ophthalmic Center in Guangdong Province, 83 from Xi'an
 First Hospital in Shanxi Province, and 60 from Guangxi Zhuang Autonomous
 Region People's Hospital in Guangxi Zhuang Autonomous
 Region.<br>There were 5 licensed ophthalmologists using standard
 processing to grade the image quality. A total of 8697 images were graded
 as good quality and were further divided into three datasets: a
 classification dataset (N=7086, including 4255 in the training set, 1413
 in the validation set, and 1418 in the test set), a segmentation dataset
 (N=56, including 46 in the training set and 10 in the test set), and an
 external validation set (N=1555, including 1455 to validate the
 classification model and 100 to validate the segmentation
 model).
description: There were 5 licensed ophthalmologists using standard processing to grade
 the image quality. A total of 8697 images were graded as good quality and
 were further divided into three datasets: a classification dataset
 (N=7086, including 4255 in the training set, 1413 in the validation set,
 and 1418 in the test set), a segmentation dataset (N=56, including 46 in
 the training set and 10 in the test set), and an external validation set
 (N=1555, including 1455 to validate the classification model and 100 to
 validate the segmentation model) (Figure 1). Clinically, three experienced
 ophthalmologists from Zhongshan Ophthalmic Center were responsible for
 ophthalmological research and clinical work. According to the results of
 fungal culture, each image was divided into five categories. Two
 ophthalmologists separately classified images of each type of fungal
 keratitis, and the third ophthalmologist reclassified the images with
 inconsistent classification results from the first two ophthalmologists.
 The labeling results were 147 for Aspergillus flavus, 3150 for Fusarium,
 182 for Mucor, 197 for Penicillium, and 3410 for images without fungal
 hypha according to the corneal cultures or corneal biopsy results. The
 image dataset was divided into three categories with a ratio of 6:2:2 for
 the training set, validation set, and test set. Before the experiment, all
 images were resized to 224224 pixels. The inverse frequency algorithm was
 used for training images to assign different weights to each type of
 image, and random horizontal flip, horizontal and vertical offset were
 applied to enhance the robustness of the network. Establishment and
 annotation of segmentation datasets. For labeling fungal hyphae, drawing
 software was used, where the brush color was set to red and the brush
 width was set to 1 pixel. Two ophthalmologists independently labeled
 fungal hyphae in 56 images to visualize them. Out of these images, 46 were
 used for the training set and 10 were used for the testing set.
 Establishment and annotation of the external validation set. The dataset
 comprises a total of 1555 images, out of which 1455 are annotated and used
 to validate the classification model. The labeling results show that there
 are 683 normal pictures and 772 pictures with fungal hyphae. Additionally,
 100 images are annotated and used to validate the segmentation model.