A large-scale chest CT dataset for COVID-19 detection, comprising 425,024 CT slices from 5,312 patients and 431,205 CT slices from 6,068 patients, respectively.
├── images # folder consisting CT scan images
├── CP_0_3136_0207.png
└── CP_1070_3112_0032.png
└── ...
├── test_COVIDx-CT.txt # test split
├── train_COVIDx-CT.txt # train split
├── val_COVIDx-CT.txt # validation split
Train set, validation set and test set are pre-split by ".txt" label files. Each line in the label files has the following format:
filename class xmin ymin xmax ymax
Classes are Normal=0, Pneumonia=1, and COVID-19=2.
Bounding boxes are given in original image coordinates, although the scope of this project does not included predicting the bounding box coordinates.
The notebook covidct-2a.ipynb walks through the process of data preparation and Deep Neural Network model training and testing.
Various backbone architectures can be selected ('vgg16', 'vgg19', 'resnet101', 'resnet152', 'densenet161', 'densenet201') and experimented with different number of layers to be frozen upon fine-tuning.
The best performance was recieved on densenet 201, using pre-trained weights of ImageNet and fine-tuned by unfreezing last 3 layers.
Data augmentations are also applied during training, for better generalization of Deep Neural Network.
After 3 epochs, following evaluation metrics were achieved :
