Deep learning-based automated COVID-19 classification from computed tomography images

Abstract

This paper introduces a lightweight Convolutional Neural Networks (CNN) method for image classification in COVID-19 diagnosis. The proposed approach emphasizes simplicity while achieving high performance, and it leverages a meticulously annotated database. The CNN model consists of four convolutional layers, followed by flattening and two dense layers. The methodology focuses on classifying 2D slices of Computed Tomography (CT) scans. To enhance accuracy, the slices undergo anatomy-relevant masking and the removal of non-representative slices from the CT volume. This is achieved by cropping a fixed-sized rectangular area to capture the relevant region of interest and using a threshold based on bright pixels in binarized slices. The proposed methodology demonstrates improved quantitative results in slice classification by employing slice processing techniques. Additionally, augmentation techniques such as class weight balancing, slice flipping, and a learning rate scheduler are applied to diagnose at the slice level. For patient-level diagnosis, a majority voting method is employed by considering the slices of each CT scan. The proposed method surpasses the baseline approach and other alternatives in terms of macro F1 score, both on the validation set and a test partition containing previously unseen images from the rigorously annotated dataset.

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