The Dataset4C.hdf5 file consists in 396 images in RGB scale (Information is only on one channel to avoid overtraining), divided in 4 classes. There are 277 training images, 59 validation images and 60 test images. (70% for training, 15% for validation and 15% for testing)
The images are getting from:
Kermany, Daniel & Goldbaum, Michael & Cai, Wenjia & Valentim, Carolina & Liang, Hui-Ying & Baxter, Sally & McKeown, Alex & Yang, Ge & Wu, Xiaokang & Yan, Fangbing & Dong, Justin & Prasadha, Made & Pei, Jacqueline & Ting, Magdalena & Zhu, Jie & Li, Christina & Hewett, Sierra & Dong, Jason & Ziyar, Ian & Zhang, Kang. (2018).
Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning.
Cell. 172. 1122-1131.e9. 10.1016/j.cell.2018.02.010.
https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia
Joseph Paul Cohen and Paul Morrison and Lan Dao
COVID-19 image data collection, arXiv:2003.11597, 2020
https://github.com/ieee8023/covid-chestxray-dataset
hdf5 file created by:
Author: Sergio Luis Beleño Díaz
Date: 2020-04-04
The classes are:
Normal: 99
Pneumonia Vir: 99
Pneumonia Bact: 99
COVID-19: 99
Labels:
Normal ==> 0
Pneumonia Vir ==> 1
Pneumonia Bact ==> 2
COVID-19 ==> 3
Size:
(512,512,3)
Information only on one channel
You can find an example of implementation in the following link:
https://github.com/Serbeld/Dataset-4C/blob/master/Data_4C.ipynb
You can download it (Information only on one channel) in the following link:
https://drive.google.com/file/d/1yLD913J3SE7drzAvnwwcv2wKAMZXIH_L/view?usp=sharing
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