COVID_MTNet: COVID-19 Detection with Multi-Task Deep Learning Approaches

TL;DR

This paper presents a multi-task deep learning framework for rapid and accurate COVID-19 detection and infected region segmentation using X-ray and CT images, achieving high accuracy and providing a novel analysis method.

Contribution

It introduces a multi-task deep learning approach combining detection and segmentation for COVID-19 using X-ray and CT scans, with novel quantitative analysis of infected regions.

Findings

Detection accuracy: 84.67% on X-ray images

Detection accuracy: 98.78% on CT images

Promising results in infection localization

Abstract

COVID-19 is currently one the most life-threatening problems around the world. The fast and accurate detection of the COVID-19 infection is essential to identify, take better decisions and ensure treatment for the patients which will help save their lives. In this paper, we propose a fast and efficient way to identify COVID-19 patients with multi-task deep learning (DL) methods. Both X-ray and CT scan images are considered to evaluate the proposed technique. We employ our Inception Residual Recurrent Convolutional Neural Network with Transfer Learning (TL) approach for COVID-19 detection and our NABLA-N network model for segmenting the regions infected by COVID-19. The detection model shows around 84.67% testing accuracy from X-ray images and 98.78% accuracy in CT-images. A novel quantitative analysis strategy is also proposed in this paper to determine the percentage of infected regions in X-ray and CT images. The qualitative and quantitative results demonstrate promising results for COVID-19 detection and infected region localization.