Application of Deep Learning Techniques for Detection of COVID-19 Using Lung CT Scans: Model Development and Validation

Coronavirus disease is a pandemic that has infected millions of people around the world. Lung CT-scans are effective diagnostic tools, but radiologists can quickly become overwhelmed by the flow of infected patients. Therefore, automated image interpretation needs to be achieved. Deep learning (DL) can support critical medical tasks including diagnostics, and DL algorithms have successfully been applied to the classification and detection of many diseases. This work aims to use deep learning methods that can classify patients between Covid-19 positive and healthy patient. We collected 4 available datasets, and tested our convolutional neural networks (CNNs) on different distributions to investigate the generalizability of our models. In order to clearly explain the predictions, Grad-CAM and Fast-CAM visualization methods were used. Our approach reaches more than 92% accuracy on 2 different distributions. In addition, we propose a computer aided diagnosis web application for Covid-19 diagnosis. The results suggest that our proposed deep learning tool can be integrated to the Covid-19 detection process and be useful for a rapid patient management.

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A Comparative Systematic Literature Review on Knee Bone Reports from MRI, X-Rays and CT Scans Using Deep Learning and Machine Learning Methodologies

Diagnostics ◽

10.3390/diagnostics10080518 ◽

2020 ◽

Vol 10 (8) ◽

pp. 518 ◽

Cited By ~ 1

Author(s):

Hafsa Khalid ◽

Muzammil Hussain ◽

Mohammed A. Al Ghamdi ◽

Tayyaba Khalid ◽

Khadija Khalid ◽

...

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Literature Review ◽

Ct Scan ◽

Systematic Literature Review ◽

Machine Learning Techniques ◽

Ct Scans ◽

X Rays ◽

X Ray ◽

Learning Techniques

The purpose of this research was to provide a “systematic literature review” of knee bone reports that are obtained by MRI, CT scans, and X-rays by using deep learning and machine learning techniques by comparing different approaches—to perform a comprehensive study on the deep learning and machine learning methodologies to diagnose knee bone diseases by detecting symptoms from X-ray, CT scan, and MRI images. This study will help those researchers who want to conduct research in the knee bone field. A comparative systematic literature review was conducted for the accomplishment of our work. A total of 32 papers were reviewed in this research. Six papers consist of X-rays of knee bone with deep learning methodologies, five papers cover the MRI of knee bone using deep learning approaches, and another five papers cover CT scans of knee bone with deep learning techniques. Another 16 papers cover the machine learning techniques for evaluating CT scans, X-rays, and MRIs of knee bone. This research compares the deep learning methodologies for CT scan, MRI, and X-ray reports on knee bone, comparing the accuracy of each technique, which can be used for future development. In the future, this research will be enhanced by comparing X-ray, CT-scan, and MRI reports of knee bone with information retrieval and big data techniques. The results show that deep learning techniques are best for X-ray, MRI, and CT scan images of the knee bone to diagnose diseases.

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COVID-19 Lung CT Image Segmentation Using Deep Learning Methods: UNET Vs. SegNET

10.21203/rs.3.rs-56882/v2 ◽

2020 ◽

Author(s):

Adnan Saood ◽

Iyad Hatem

Keyword(s):

Deep Learning ◽

Infection Type ◽

Disease Diagnosis ◽

Infected Tissue ◽

Scene Segmentation ◽

Learning Networks ◽

Learning Techniques ◽

Computer Based ◽

Feasible Solutions ◽

Lung Ct

Abstract Background: Currently, there is an urgent need for efficient tools to assess the diagnosis of COVID-19patients. In this paper, we present feasible solutions for detecting and labeling infected tissues on CT lungimages of such patients. Two structurally-different deep learning techniques, SegNet and UNET, areinvestigated for semantically segmenting infected tissue regions in CT lung images.Methods: We propose to use two known deep learning networks, SegNet and UNET, for image tissueclassification. SegNet is characterized as as scene segmentation network and UNET as a medical segmentationtool. Both networks were exploited as binary segmentors to discriminate between infected and healthy lungtissue, also as multi-class segmentors to learn the infection type on the lung. Each network is trained usingseventy-two data images, validated on ten images, and tested against the left eighteen images. Severalstatistical scores are calculated for the results and tabulated accordingly.Results: The results show the superior ability of SegNet in classifying infected/non-infected tissues comparedto the other methods (with 0:95 mean accuracy), while the UNET shows better results as a multi-classsegmentor (with 0:91 mean accuracy).Conclusion: Semantically segmenting CT scan images of COVID-19 patients is a crucial goal because it wouldnot only assist in disease diagnosis, also help in quantifying the severity of the illness, and hence, prioritize thepopulation treatment accordingly. We propose computer-based techniques that prove to be reliable asdetectors for infected tissue in lung CT scans. The availability of such a method in today’s pandemic wouldhelp automate, prioritize, fasten, and broaden the treatment of COVID-19 patients globally.

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Deep CNN-Based CAD System for COVID-19 Detection Using Multiple Lung CT Scans (Preprint)

10.2196/preprints.27468 ◽

2021 ◽

Author(s):

Mustafa Ghaderzadeh ◽

Farkhondeh Asadi ◽

Ramezan Jafari ◽

Davood Bashash ◽

Hassan Abolghasemi ◽

...

Keyword(s):

Deep Learning ◽

Image Feature ◽

Detection Sensitivity ◽

World Health ◽

Detection Methods ◽

Ct Scans ◽

Cad System ◽

Early Stages ◽

The World ◽

Lung Ct

BACKGROUND Due to the COVID-19 pandemic and the imminent collapse of healthcare systems following the excessive consumption of financial, hospital, and medicinal resources, the World Health Organization (WHO) changed the alert level on the COVID-19 pandemic from high to very high. Meanwhile, the world began to favor less expensive and more precise COVID-19 detection methods. Machine vision-based COVID-19 detection methods especially Deep learning as a diagnostic technique in the early stages of the disease have found great importance during the pandemic. OBJECTIVE This study aimed to design a highly efficient Computer-Aided Detection (CAD) system for COVID-19 by using a NASNet-based algorithm. n images of 190 persons suspected of COVID-19, was used. METHODS A state-of-the-art pre-trained CNN network for image feature extraction, called NASNet, was adopted to identify patients with COVID-19 in the first stages of the disease. A local dataset, comprising 10153 CT scan images of 190 persons suspected of COVID-19, was used. RESULTS After fitting on the training dataset, hyper-parameter tuning and finally topological alterations of the classifier block, the proposed NASNet-based model was evaluated on the test dataset and yielded remarkable results. The proposed model's performance achieved a detection sensitivity, specificity, and accuracy of 0.999, 0.986, and 0.996, respectively. CONCLUSIONS The proposed model achieved acceptable results in the categorization of two data classes. Therefore, a CAD system was designed based on this model for COVID-19 detection using multiple lung CT scans. The system managed to differentiate all the COVID-19 cases from non-COVID-19 ones without any error in the application phase. Overall, the proposed deep learning-based CAD system can greatly aid radiologists in the detection of COVID-19 in its early stages. During the COVID-19 pandemic, the use of CAD system as a screening tool accelerates the process of disease detection and prevents the loss of healthcare resources.

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