salehhariyanti / corona-net

Fighting COVID-19 with Computer Vision

Corona-Net: Diagnosis and Segmentation of the CoronavirusDisease 2019

Introduction

Current baselines in biomedical image segmentation utilise fully-convolutional structures for the benefits of end-to-end trainability, size-invariance and efficiency. One such method is U-Net [1], a two-track contraction-expansion model which fuses features at different hierarchies with the objective of generating deep localisable features. Here, I introduce Corona-Net, a 3-part contribution dedicated to the classification, binary segmentation and multi-class segmentation of COVID-19. I first leverage the EfficientNet model [2] for COVID-19 diagnosis, achieving an accuracy of 93.89. I then utilise and refine the U-Net architecture for both binary and 3-class (ground-glass, consolidation, pleural effusion) segmentation of COVID-19 symptoms, through inference on the 100-slice COVID-19 CT segmentation (chest axial CT) dataset 1, and 630-slice COVID-19 CT segmentation dataset 2 [3]. Through strong data augmentation and rigorous experimentation, I overcome the small dataset size (630) to achieve a Dice Loss of 79.65% (dataset 2) and 61.60% (dataset 1). Through Corona-Net, I aim to develop a reliable, visual-semantically balanced method for automatic COVID-19 diagnosis confirmation, in order to contribute to the fight against this pandemic.

Results

1. Classification

Model	1- BCE Loss	Optimiser	Learning Rate
Efficient-Net-b0	0.9237	Adam	1e-05
Efficient-Net-b1	0.9389	Adam	1e-05
Efficient-Net-b2	0.9085	Adam	1e-05

2. Binary Segmentation

Dice Coefficient	Rand Loss	Optimiser	Learning Rate
0.5641	0.2167	Adam	1e-02
0.7374	0.1031	Adam	1e-03
0.7965	0.0766	Adam	1e-04
0.4745	0.1591	Adam	1e-05

3. Multi-Class Segmentation

Dice Coefficient	Rand Loss	Optimiser	Learning Rate
0.5160	0.2490	Adam	1e-02
0.5900	0.2114	Adam	1e-03
0.6160	0.1985	Adam	1e-04
0.5001	0.2565	Adam	1e-05

Credits

[1] O. Ronneberger, P. Fischer, and T. Brox. U-Net:Convolutional Networks for Biomedical ImageSegmentation. INMICCAI, 2015. [2] M. Tan and Q. V. Le. EfficientNet: Rethink-ing Model Scaling for Convolutional Neural Net-works In ICML, 2019.
[3] Medical Segmentation.com COVID-19 CT seg-mentation dataset, 2020.
[4] M. Buda, A. Saha, and M. A. Mazurowski. Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm. In Computers in Biology and Medicine, 2019.