A General Preprocessing Pipeline for Deep Learning on Radiology Images: A COVID-19 Case Study

Abstract

During the last years, deep learning has been used intensively in medical domain making considerable progress in the diagnosis of diseases from radiology images. This is mainly due to the availability of proven algorithms on several computer vision tasks and the publicly accessible medical datasets. However, most approaches that apply deep learning techniques to radiology images transform these images into a format that conforms with the inputs of conventional learning algorithms and deal with the dataset as a set of 2D independent slices instead of volumetric images. In this work we deal with the problem of preparing DICOM CT scans as 3D images for a machine learning/deep learning architecture. We propose a general preprocessing pipeline composed of four stages for volumetric images processing followed by a 3D CNN architecture for 3D images classification. The proposed pipeline is evaluated through a case study for COVID-19 detection from chest CT scans. Experiment results demonstrate the effectiveness of the proposed preprocessing operations.