Pneumonia Detection in Chest X-ray Image with EfficientNet-B7

Google has published both a very exciting paper and source code for a newly designed CNN called EfficientNet, that set new records for both accuracy and computational efficiency over most popular CNNs on ImageNet dataset. Among them, EfficientNet-B7 (which achieved highest accuracy over ImageNet dataset) is used here to solve a classsification problem, detection of Pneumonia in Chest X-ray Images. Without any augmentation, 100% precision can be achieved.

Database

Kermany D., Goldbaum M., Cai W. Large dataset of labeled optical coherence tomography (OCT) and chest X-Ray images 2018, 172, 1122–1131. Cell. 2018;172:1122–1131. doi: 10.1016/j.cell.2018.02.010. https://data.mendeley.com/datasets/rscbjbr9sj/3

Unzip ZhangLabData.zip
Copy it to "./data/" directory

Training Dataset:

Total Images: 5233
Normal Healthy Person: 1349
Pnumonia Patients: 3884

Testing Dataset:

Total Images: 624
Normal Healthy Person: 234
Pnumonia Patients: 390

Validation Dataset is created from Training Dataset for calibrating Hyperparameters.

Codebase

Driver Program

train.py = runs training session
test.py = runs testing session
folder_to_csv.py = lists files in a folder
merge_csv.py = merges contents in CSV files
augmentation.py = creates augmented dataset

Setting File

settings.json = contains hyperparameters

Utility Classes

_datagen.py = data generator for deep learning session
_train_test.py = runs deep learning session

EfficientNet by Luke Melas-Kyriazi https://github.com/lukemelas/EfficientNet-PyTorch

./efficientNet/

Result

Plain Data (No Augmentation)

Accuracy = 87.98%
Precision = 100%
Recall = 83.87%
F1 Score = 91.23