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

Abstract

We present an automatic COVID1-19 diagnosis framework from lung CT-scan slice images. In this framework, the slice images of a CT-scan volume are first proprocessed using segmentation techniques to filter out images of closed lung, and to remove the useless background. Then a resampling method is used to select a set of fixed number of slice images for training and validation. A 3D CNN network with BERT is used to classify this set of selected slice images. In this network, an embedding feature is also extracted. In cases where there are more than one set of slice images in a volume, the features of all sets are extracted and pooled into a feature vector for the whole CT-scan volume. A simple multiple-layer perceptron (MLP) network is to used to further classify the aggregated feature vector. The models are trained and evaluated on the provided training and validation datasets. On the validation dataset, the precision is 0.906 and the F1 score is 0.89.

COVID19 Diagnosis using AutoML from 3D CT scans

“…W.Tan and J.Liu used 3D CNN with BERT to extract features and MLP classifiers to classify the data. The authors used the COV19-CT-DB data and achieve an F1-score of 89% [16]. The current paper discussed the AtuoML approach to diagnose COVID from CT-scan images.…”