Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

COVID-19 Diagnosis from chest CT Scans: A Weakly Supervised CNN-LSTM Approach

Version 1 : Received: 21 May 2021 / Approved: 25 May 2021 / Online: 25 May 2021 (10:32:29 CEST)

How to cite: Kara, M.; Öztürk, Z.; Akpek, S.; Turupcu, A. COVID-19 Diagnosis from chest CT Scans: A Weakly Supervised CNN-LSTM Approach. Preprints 2021, 2021050605 (doi: 10.20944/preprints202105.0605.v1). Kara, M.; Öztürk, Z.; Akpek, S.; Turupcu, A. COVID-19 Diagnosis from chest CT Scans: A Weakly Supervised CNN-LSTM Approach. Preprints 2021, 2021050605 (doi: 10.20944/preprints202105.0605.v1).

Abstract

Advancements in deep learning and availability of medical imaging data have led to use of CNN based architectures in disease diagnostic assisted systems. In spite of the abundant use of reverse transcription-polymerase chain reaction (RT-PCR) based tests in COVID-19 diagnosis, CT images offer an applicable supplement with its high sensitivity rates. Here, we study classification of COVID-19 pneumonia (CP) and non-COVID-19 pneumonia (NCP) in chest CT scans using efficient deep learning methods to be readily implemented by any hospital. We report our deep network framework design that encompasses Convolutional Neural Networks (CNNs) and bidirectional Long Short Term Memory (biLSTM) architectures. Our study achieved high specificity (CP: 98.3%, NCP: 96.2% Healthy: 89.3%) and high sensitivity (CP: 84.0%, NCP: 93.9% Healthy: 94.9%) in classifying COVID-19 pneumonia, non-COVID-19 pneumonia and healthy patients. Next, we provide visual explanations for the CNN predictions with gradient-weighted class activation mapping (Grad-CAM). The results provided a model explainability by showing that Ground Glass Opacities (GGO), indicators of COVID-19 pneumonia disease, were captured by our CNN network. Finally, we have implemented our approach in three hospitals proving its compatibility and efficiency.

Subject Areas

deep learning; computed tomography; image classification; COVID-19; medical image analysis; pneumonia; CNN, LSTM, medical diagnosis

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