jefftianzy / covid-19-cbamresnet18-classification

A simple network module for pneumonia x-ray classification

COVID-19-CBAMResNet18-Classification

A simple network module for pneumonia x-ray classification

• Overall Accuracy: 96.8%
• COVID-19 Recall/Precistion: 100%

Abstract

A three-category classifier for pneumonia x-ray that distiguish non-pneumonia, normal pneumonia and COVID-19. We added CBAM (Convolutional Block Attention Module) before the first layer and after the last conbolutional layer of ResNet18 that significantly enhanced its ability.

Datasets

Dataset 1: covid-chestxray-dataset(only COVID-19 data)

https://github.com/ieee8023/covid-chestxray-dataset

Dataset 2: CoronaHack-Chest X-Ray-Dataset(only normal pneumonia and non-pneumonia data)

https://www.kaggle.com/praveengovi/coronahack-chest-xraydataset

Integration pack: (contains all data above)

BaiduNetdisk: https://pan.baidu.com/s/11BwGCB1n2rQvHGc137_oLg

Password: dw7a

Experiments

ResNet18 Classfication

We trained a ResNet18 model on the previous dataset by finetuning weights from Imagenet (Batch_size=24, Epoch=50, optim=Adam, learning_rate=0.001, criterion=CrossEntropy).

Results are as follows:

ResNet18	Precision	Recall	F1-score	Num
Normal	0.94	0.88	0.91	151
Pneumonia	0.95	0.99	0.97	411
COVID-19	1.00	0.70	0.82	23

CBAMResNet18 Classfication

We then added CBAM to the network with the same hyperparameter.

Results are as follows:

CBAMResNet18	Precision	Recall	F1-score	Num
Normal	0.93	0.95	0.94	151
Pneumonia	0.98	0.97	0.98	411
COVID-19	1.00	1.00	1.00	23

Visualization

By using Grad-CAM, we can visualize the contribution of CBAM (in folder)

covid-19-cbamresnet18-classification's Issues

from CBAM_inception import CBAMInceptionResidual_18

未解析的引用CBAM_inception