scholarly journals DIAG a Diagnostic Web Application Based on Lung CT Scan Images and Deep Learning

2021 ◽  
Author(s):  
Amel Imene Hadj Bouzid ◽  
Said Yahiaoui ◽  
Anis Lounis ◽  
Sid-Ahmed Berrani ◽  
Hacène Belbachir ◽  
...  
Keyword(s):  
Deep Learning ◽  
Web Application ◽  
Patient Management ◽  
Image Interpretation ◽  
Ct Scans ◽  
Diagnostic Tools ◽  
The World ◽  
Aided Diagnosis ◽  
Lung Ct ◽  
Lung Ct Scan

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.

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

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.

scholarly journals Automated Detection of COVID-19 using Deep Learning

2021 ◽  
Vol 10 (2) ◽  
pp. , 824-830
Keyword(s):  
Public Health ◽  
Deep Learning ◽  
Global Economy ◽  
Imaging Techniques ◽  
Automated Detection ◽  
Diagnostic Tools ◽  
Radiological Imaging ◽  
Daily Lives ◽  
Learning Techniques ◽  
The World

Corona virus 2019 (COVID-2019), has first appeared in Wuhan, China in December 2019, spread around the world rapidly causing thousands of fatalities. It is caused a devastating result in our daily lives, public health, and also the global economy. It is important to sight the positive cases as early as possible therefore forestall any unfoldment of this epidemic and to quickly treat affected patients. The necessity for auxiliary diagnostic tools has increased as there aren't any accurate automated toolkits available. Recent findings obtained using radiology imaging techniques suggest that such images contain salient information about the COVID-19 virus. Coupling deep learning techniques with radiological imaging may end up within the accurate detection of this disease. This assistance will help to beat the matter of an absence of specialized physicians in the remote villages.

scholarly journals Deep-learning algorithms for the interpretation of chest radiographs to aid in the triage of COVID-19 patients: A multicenter retrospective study

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0242759
Author(s):  
Se Bum Jang ◽  
Suk Hee Lee ◽  
Dong Eun Lee ◽  
Sin-Youl Park ◽  
Jong Kun Kim ◽  
...  
Keyword(s):  
Deep Learning ◽  
Retrospective Study ◽  
Image Interpretation ◽  
Area Under The Curve ◽  
Ground Glass Opacity ◽  
Community Treatment ◽  
Ct Scans ◽  
Treatment Center ◽  
Automatic Interpretation ◽  
Radiology Reports

The recent medical applications of deep-learning (DL) algorithms have demonstrated their clinical efficacy in improving speed and accuracy of image interpretation. If the DL algorithm achieves a performance equivalent to that achieved by physicians in chest radiography (CR) diagnoses with Coronavirus disease 2019 (COVID-19) pneumonia, the automatic interpretation of the CR with DL algorithms can significantly reduce the burden on clinicians and radiologists in sudden surges of suspected COVID-19 patients. The aim of this study was to evaluate the efficacy of the DL algorithm for detecting COVID-19 pneumonia on CR compared with formal radiology reports. This is a retrospective study of adult patients that were diagnosed as positive COVID-19 cases based on the reverse transcription polymerase chain reaction among all the patients who were admitted to five emergency departments and one community treatment center in Korea from February 18, 2020 to May 1, 2020. The CR images were evaluated with a publicly available DL algorithm. For reference, CR images without chest computed tomography (CT) scans classified as positive for COVID-19 pneumonia were used given that the radiologist identified ground-glass opacity, consolidation, or other infiltration in retrospectively reviewed CR images. Patients with evidence of pneumonia on chest CT scans were also classified as COVID-19 pneumonia positive outcomes. The overall sensitivity and specificity of the DL algorithm for detecting COVID-19 pneumonia on CR were 95.6%, and 88.7%, respectively. The area under the curve value of the DL algorithm for the detection of COVID-19 with pneumonia was 0.921. The DL algorithm demonstrated a satisfactory diagnostic performance comparable with that of formal radiology reports in the CR-based diagnosis of pneumonia in COVID-19 patients. The DL algorithm may offer fast and reliable examinations that can facilitate patient screening and isolation decisions, which can reduce the medical staff workload during COVID-19 pandemic situations.

A survey of computer-aided diagnosis of lung nodules from CT scans using deep learning

2021 ◽  
pp. 104806
Author(s):  
Yu Gu ◽  
Jingqian Chi ◽  
Jiaqi Liu ◽  
Lidong Yang ◽  
Baohua Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Computer Aided Diagnosis ◽  
Ct Scans ◽  
Lung Nodules ◽  
Computer Aided ◽  
Aided Diagnosis

scholarly journals A deep learning algorithm to detect coronavirus (COVID-19) disease using CT images

2021 ◽  
Vol 7 ◽  
pp. e345
Author(s):  
Mojtaba Mohammadpoor ◽  
Mehran Sheikhi karizaki ◽  
Mina Sheikhi karizaki
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Screening Method ◽  
Ct Scans ◽  
X Rays ◽  
Deep Learning Algorithm ◽  
Fast Screening ◽  
Proposed Model ◽  
Deep Learning Neural Network ◽  
Lung Ct

Background COVID-19 pandemic imposed a lockdown situation to the world these past months. Researchers and scientists around the globe faced serious efforts from its detection to its treatment. Methods Pathogenic laboratory testing is the gold standard but it is time-consuming. Lung CT-scans and X-rays are other common methods applied by researchers to detect COVID-19 positive cases. In this paper, we propose a deep learning neural network-based model as an alternative fast screening method that can be used for detecting the COVID-19 cases by analyzing CT-scans. Results Applying the proposed method on a publicly available dataset collected of positive and negative cases showed its ability on distinguishing them by analyzing each individual CT image. The effect of different parameters on the performance of the proposed model was studied and tabulated. By selecting random train and test images, the overall accuracy and ROC-AUC of the proposed model can easily exceed 95% and 90%, respectively, without any image pre-selecting or preprocessing.

scholarly journals Towards Detecting High-Uptake Lesions from Lung CT Scans Using Deep Learning

2017 ◽  
pp. 310-320 ◽  
Author(s):  
Krzysztof Pawełczyk ◽  
Michal Kawulok ◽  
Jakub Nalepa ◽  
Michael P. Hayball ◽  
Sarah J. McQuaid ◽  
...  
Keyword(s):  
Deep Learning ◽  
Ct Scans ◽  
High Uptake ◽  
Lung Ct

scholarly journals Design of Optimal Deep Learning-Based Pancreatic Tumor and Nontumor Classification Model Using Computed Tomography Scans

2022 ◽  
Vol 2022 ◽  
pp. 1-15
Author(s):  
Maha M. Althobaiti ◽  
Ahmed Almulihi ◽  
Amal Adnan Ashour ◽  
Romany F. Mansour ◽  
Deepak Gupta
Keyword(s):  
Deep Learning ◽  
Pancreatic Tumor ◽  
Image Interpretation ◽  
Classification Model ◽  
Pancreatic Tumors ◽  
Computed Tomography Scans ◽  
Current Scenario ◽  
Comparative Results ◽  
Study Designs ◽  
Aided Diagnosis

Pancreatic tumor is a lethal kind of tumor and its prediction is really poor in the current scenario. Automated pancreatic tumor classification using computer-aided diagnosis (CAD) model is necessary to track, predict, and classify the existence of pancreatic tumors. Artificial intelligence (AI) can offer extensive diagnostic expertise and accurate interventional image interpretation. With this motivation, this study designs an optimal deep learning based pancreatic tumor and nontumor classification (ODL-PTNTC) model using CT images. The goal of the ODL-PTNTC technique is to detect and classify the existence of pancreatic tumors and nontumor. The proposed ODL-PTNTC technique includes adaptive window filtering (AWF) technique to remove noise existing in it. In addition, sailfish optimizer based Kapur’s Thresholding (SFO-KT) technique is employed for image segmentation process. Moreover, feature extraction using Capsule Network (CapsNet) is derived to generate a set of feature vectors. Furthermore, Political Optimizer (PO) with Cascade Forward Neural Network (CFNN) is employed for classification purposes. In order to validate the enhanced performance of the ODL-PTNTC technique, a series of simulations take place and the results are investigated under several aspects. A comprehensive comparative results analysis stated the promising performance of the ODL-PTNTC technique over the recent approaches.

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