Data Augmentation and CNN Classification For Automatic COVID-19 Diagnosis From CT-Scan Images On Small Dataset

08/16/2021
by   Weijun Tan, et al.
0

We present an automatic COVID1-19 diagnosis framework from lung CT images. The focus is on signal processing and classification on small datasets with efforts putting into exploring data preparation and augmentation to improve the generalization capability of the 2D CNN classification models. We propose a unique and effective data augmentation method using multiple Hounsfield Unit (HU) normalization windows. In addition, the original slice image is cropped to exclude background, and a filter is applied to filter out closed-lung images. For the classification network, we choose to use 2D Densenet and Xception with the feature pyramid network (FPN). To further improve the classification accuracy, an ensemble of multiple CNN models and HU windows is used. On the training/validation dataset, we achieve a patient classification accuracy of 93.39

READ FULL TEXT

page 2

page 3

research
06/28/2021

A 3D CNN Network with BERT For Automatic COVID-19 Diagnosis From CT-Scan Images

We present an automatic COVID1-19 diagnosis framework from lung CT-scan ...
research
03/03/2018

Chest X-Ray Analysis of Tuberculosis by Deep Learning with Segmentation and Augmentation

The results of chest X-ray (CXR) analysis of 2D images to get the statis...
research
04/07/2020

Pyramid Focusing Network for mutation prediction and classification in CT images

Predicting the mutation status of genes in tumors is of great clinical s...
research
06/29/2022

Two-Stage COVID19 Classification Using BERT Features

We propose an automatic COVID1-19 diagnosis framework from lung CT-scan ...
research
01/07/2018

Anatomical Data Augmentation For CNN based Pixel-wise Classification

In this work we propose a method for anatomical data augmentation that i...
research
11/10/2022

MixUp-MIL: Novel Data Augmentation for Multiple Instance Learning and a Study on Thyroid Cancer Diagnosis

Multiple instance learning exhibits a powerful approach for whole slide ...

Please sign up or login with your details

Forgot password? Click here to reset