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 An Investigation into the Application of Deep Learning in the Detection and Mitigation of DDOS Attack on SDN Controllers

Technologies ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 14
Author(s):  
James Dzisi Gadze ◽  
Akua Acheampomaa Bamfo-Asante ◽  
Justice Owusu Agyemang ◽  
Henry Nunoo-Mensah ◽  
Kwasi Adu-Boahen Opare
Keyword(s):  
Deep Learning ◽  
Network Architecture ◽  
Learning Algorithm ◽  
Single Point ◽  
Denial Of Service ◽  
Specific Work ◽  
Learning Models ◽  
Ddos Attack ◽  
Deep Learning Algorithm ◽  
Proposed Model

Software-Defined Networking (SDN) is a new paradigm that revolutionizes the idea of a software-driven network through the separation of control and data planes. It addresses the problems of traditional network architecture. Nevertheless, this brilliant architecture is exposed to several security threats, e.g., the distributed denial of service (DDoS) attack, which is hard to contain in such software-based networks. The concept of a centralized controller in SDN makes it a single point of attack as well as a single point of failure. In this paper, deep learning-based models, long-short term memory (LSTM) and convolutional neural network (CNN), are investigated. It illustrates their possibility and efficiency in being used in detecting and mitigating DDoS attack. The paper focuses on TCP, UDP, and ICMP flood attacks that target the controller. The performance of the models was evaluated based on the accuracy, recall, and true negative rate. We compared the performance of the deep learning models with classical machine learning models. We further provide details on the time taken to detect and mitigate the attack. Our results show that RNN LSTM is a viable deep learning algorithm that can be applied in the detection and mitigation of DDoS in the SDN controller. Our proposed model produced an accuracy of 89.63%, which outperformed linear-based models such as SVM (86.85%) and Naive Bayes (82.61%). Although KNN, which is a linear-based model, outperformed our proposed model (achieving an accuracy of 99.4%), our proposed model provides a good trade-off between precision and recall, which makes it suitable for DDoS classification. In addition, it was realized that the split ratio of the training and testing datasets can give different results in the performance of a deep learning algorithm used in a specific work. The model achieved the best performance when a split of 70/30 was used in comparison to 80/20 and 60/40 split ratios.

scholarly journals Deep Learning Based Automated Orthotopic Lung Tumor Segmentation in Whole-Body Mouse CT-Scans

Cancers ◽  
2021 ◽  
Vol 13 (18) ◽  
pp. 4585
Author(s):  
Wouter R. P. H. van de Worp ◽  
Brent van der Heyden ◽  
Georgios Lappas ◽  
Ardy van Helvoort ◽  
Jan Theys ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Deep Learning ◽  
Tumor Growth ◽  
Tumor Volume ◽  
Learning Algorithm ◽  
Lung Tumor ◽  
Whole Body ◽  
Ct Scans ◽  
Therapeutic Effects ◽  
Deep Learning Algorithm

Lung cancer is the leading cause of cancer related deaths worldwide. The development of orthotopic mouse models of lung cancer, which recapitulates the disease more realistically compared to the widely used subcutaneous tumor models, is expected to critically aid the development of novel therapies to battle lung cancer or related comorbidities such as cachexia. However, follow-up of tumor take, tumor growth and detection of therapeutic effects is difficult, time consuming and requires a vast number of animals in orthotopic models. Here, we describe a solution for the fully automatic segmentation and quantification of orthotopic lung tumor volume and mass in whole-body mouse computed tomography (CT) scans. The goal is to drastically enhance the efficiency of the research process by replacing time-consuming manual procedures with fast, automated ones. A deep learning algorithm was trained on 60 unique manually delineated lung tumors and evaluated by four-fold cross validation. Quantitative performance metrics demonstrated high accuracy and robustness of the deep learning algorithm for automated tumor volume analyses (mean dice similarity coefficient of 0.80), and superior processing time (69 times faster) compared to manual segmentation. Moreover, manual delineations of the tumor volume by three independent annotators was sensitive to bias in human interpretation while the algorithm was less vulnerable to bias. In addition, we showed that besides longitudinal quantification of tumor development, the deep learning algorithm can also be used in parallel with the previously published method for muscle mass quantification and to optimize the experimental design reducing the number of animals needed in preclinical studies. In conclusion, we implemented a method for fast and highly accurate tumor quantification with minimal operator involvement in data analysis. This deep learning algorithm provides a helpful tool for the noninvasive detection and analysis of tumor take, tumor growth and therapeutic effects in mouse orthotopic lung cancer models.

scholarly journals Performance of a deep learning CNN model for the automated detection of 13 common conditions on Chest X-rays

2021 ◽  
Author(s):  
Tirupathi Karthik ◽  
Vijayalakshmi Kasiraman ◽  
Bhavani Paski ◽  
Kashyap Gurram ◽  
Amit Talwar ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Delayed Diagnosis ◽  
Cost Effective ◽  
Radiology Workflow ◽  
X Rays ◽  
Term Care ◽  
X Ray ◽  
Intelligence System ◽  
Deep Learning Algorithm

Background and aims: Chest X-rays are widely used, non-invasive, cost effective imaging tests. However, the complexity of interpretation and global shortage of radiologists have led to reporting backlogs, delayed diagnosis and a compromised quality of care. A fully automated, reliable artificial intelligence system that can quickly triage abnormal images for urgent radiologist review would be invaluable in the clinical setting. The aim was to develop and validate a deep learning Convoluted Neural Network algorithm to automate the detection of 13 common abnormalities found on Chest X-rays. Method: In this retrospective study, a VGG 16 deep learning model was trained on images from the Chest-ray 14, a large publicly available Chest X-ray dataset, containing over 112,120 images with annotations. Images were split into training, validation and testing sets and trained to identify 13 specific abnormalities. The primary performance measures were accuracy and precision. Results: The model demonstrated an overall accuracy of 88% in the identification of abnormal X-rays and 87% in the detection of 13 common chest conditions with no model bias. Conclusion: This study demonstrates that a well-trained deep learning algorithm can accurately identify multiple abnormalities on X-ray images. As such models get further refined, they can be used to ease radiology workflow bottlenecks and improve reporting efficiency. Napier Healthcare’s team that developed this model consists of medical IT professionals who specialize in AI and its practical application in acute & long-term care settings. This is currently being piloted in a few hospitals and diagnostic labs on a commercial basis.

scholarly journals TRACKING AND PREDICTING COVID-19 RADIOLOGICAL TRAJECTORY USING DEEP LEARNING ON CHEST X-RAYS: INITIAL ACCURACY TESTING

2020 ◽  
Author(s):  
S. Duchesne ◽  
D. Gourdeau ◽  
P. Archambault ◽  
C. Chartrand-Lefebvre ◽  
L. Dieumegarde ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Roc Curves ◽  
Quantitative Information ◽  
Lung Lesion ◽  
Imaging Features ◽  
X Rays ◽  
Disease Trajectory ◽  
Deep Learning Algorithm ◽  
Deep Learning Features

ABSTRACTBackgroundDecision scores and ethically mindful algorithms are being established to adjudicate mechanical ventilation in the context of potential resources shortage due to the current onslaught of COVID-19 cases. There is a need for a reproducible and objective method to provide quantitative information for those scores.PurposeTowards this goal, we present a retrospective study testing the ability of a deep learning algorithm at extracting features from chest x-rays (CXR) to track and predict radiological evolution.Materials and MethodsWe trained a repurposed deep learning algorithm on the CheXnet open dataset (224,316 chest X-ray images of 65,240 unique patients) to extract features that mapped to radiological labels. We collected CXRs of COVID-19-positive patients from two open-source datasets (last accessed on April 9, 2020)(Italian Society for Medical and Interventional Radiology and MILA). Data collected form 60 pairs of sequential CXRs from 40 COVID patients (mean age ± standard deviation: 56 ± 13 years; 23 men, 10 women, seven not reported) and were categorized in three categories: “Worse”, “Stable”, or “Improved” on the basis of radiological evolution ascertained from images and reports. Receiver operating characteristic analyses, Mann-Whitney tests were performed.ResultsOn patients from the CheXnet dataset, the area under ROC curves ranged from 0.71 to 0.93 for seven imaging features and one diagnosis. Deep learning features between “Worse” and “Improved” outcome categories were significantly different for three radiological signs and one diagnostic (“Consolidation”, “Lung Lesion”, “Pleural effusion” and “Pneumonia”; all P < 0.05). Features from the first CXR of each pair could correctly predict the outcome category between “Worse” and “Improved” cases with 82.7% accuracy.ConclusionCXR deep learning features show promise for classifying the disease trajectory. Once validated in studies incorporating clinical data and with larger sample sizes, this information may be considered to inform triage decisions.

scholarly journals Development of Deep Learning Algorithm for Brain Tumor Segmentation

2019 ◽  
Vol 9 (1) ◽  
pp. 2800-2803
Keyword(s):  
Deep Learning ◽  
Brain Tumor ◽  
Brain Tumors ◽  
Learning Algorithm ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Human Errors ◽  
Deep Learning Algorithm ◽  
Proposed Model ◽  
A New Technique

Medical imaging is an emerging field in engineering. As traditional way of brain tumor analysis, MRI scanning is the way to identify brain tumor. The core drawback of manual MRI studies conducted by surgeons is getting manual visual errorswhich can lead toofa false identification of tumor boundaries. To avoid such human errors, ultra age engineering adopted deep learning as a new technique for brain tumor segmentation. Deep learning convolution network can be further developed by means of various deep learning models for better performance. Hence, we proposed a new deep learning algorithm development which can more efficiently identifies the types of brain tumors in terms of level of tumor like T1, T2, and T1ce etc. The proposed system can identify tumors using convolution neural network(CNN) which works with the proposed algorithm “Sculptor DeepCNet”. The proposed model can be used by surgeons to identify post-surgical remains (if any) of brain tumors and thus proposed research can be useful for ultra-age neural surgical image assessments. This paper discusses newly developed algorithm and its testing results.

scholarly journals Explainable COVID-19 Detection on Chest X-rays Using an End-to-End Deep Convolutional Neural Network Architecture

2021 ◽  
Vol 5 (4) ◽  
pp. 73
Author(s):  
Mohamed Chetoui ◽  
Moulay A. Akhloufi ◽  
Bardia Yousefi ◽  
El Mostafa Bouattane
Keyword(s):  
Deep Learning ◽  
Medical Imaging ◽  
Network Architecture ◽  
Learning Algorithm ◽  
Binary Classification ◽  
X Rays ◽  
Deep Learning Algorithm ◽  
Binary Model ◽  
Chest X Ray ◽  
Multi Class Classification

The coronavirus pandemic is spreading around the world. Medical imaging modalities such as radiography play an important role in the fight against COVID-19. Deep learning (DL) techniques have been able to improve medical imaging tools and help radiologists to make clinical decisions for the diagnosis, monitoring and prognosis of different diseases. Computer-Aided Diagnostic (CAD) systems can improve work efficiency by precisely delineating infections in chest X-ray (CXR) images, thus facilitating subsequent quantification. CAD can also help automate the scanning process and reshape the workflow with minimal patient contact, providing the best protection for imaging technicians. The objective of this study is to develop a deep learning algorithm to detect COVID-19, pneumonia and normal cases on CXR images. We propose two classifications problems, (i) a binary classification to classify COVID-19 and normal cases and (ii) a multiclass classification for COVID-19, pneumonia and normal. Nine datasets and more than 3200 COVID-19 CXR images are used to assess the efficiency of the proposed technique. The model is trained on a subset of the National Institute of Health (NIH) dataset using swish activation, thus improving the training accuracy to detect COVID-19 and other pneumonia. The models are tested on eight merged datasets and on individual test sets in order to confirm the degree of generalization of the proposed algorithms. An explainability algorithm is also developed to visually show the location of the lung-infected areas detected by the model. Moreover, we provide a detailed analysis of the misclassified images. The obtained results achieve high performances with an Area Under Curve (AUC) of 0.97 for multi-class classification (COVID-19 vs. other pneumonia vs. normal) and 0.98 for the binary model (COVID-19 vs. normal). The average sensitivity and specificity are 0.97 and 0.98, respectively. The sensitivity of the COVID-19 class achieves 0.99. The results outperformed the comparable state-of-the-art models for the detection of COVID-19 on CXR images. The explainability model shows that our model is able to efficiently identify the signs of COVID-19.

scholarly journals Highly Robust Prediction of Lung Nodule Malignancy by Deep Learning Model: A Multiracial, Multinational Study

2020 ◽  
Author(s):  
Hao Wu ◽  
Wen Tang ◽  
Chu Wu ◽  
Yufeng Deng ◽  
Rongguo Zhang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Clinical Features ◽  
Learning Algorithm ◽  
Lung Nodule ◽  
Ct Scans ◽  
Multivariate Logistic Regression Model ◽  
Lung Nodules ◽  
Deep Learning Algorithm

AbstractPurposeAlthough statistical models have been employed to detect and classify lung nodules using deep learning-extracted and clinical features, there is a lack of model validation in independent, multinational datasets from computed tomography (CT) scans and patient clinical information. To this end, we developed a deep learning-based algorithm to predict the malignancy of pulmonary nodules and validated its performance in three independent datasets containing multiracial and multinational populations.MethodsIn this study, a convolutional neural network-based algorithm to predict lung nodule malignancy was built based on CT scans and patient-wise clinical features (i.e. sex, spiculation, and nodule location). The model consists of three steps: (1) a deep learning algorithm to automatically extract features from CT scans, (2) clinical features were concatenated with the nodule features after dimension reduction by the principal component analysis (PCA), and (3) a multivariate logistic regression model was employed to classify the malignancy of the lung nodules. The model was trained by a dataset containing 1,556 nodules from 813 patients from the National Lung Screening Trial (NLST). The performance of the model was evaluated on three independent, multi-institutional datasets LIDC and Infervision Multi-Center (IMC) dataset, which contains 562 nodules from 293 patients, and 2044 nodules from 589 patients, respectively. The model accuracy was measured by the area under curve (AUC) of receiver operating characteristic (ROC) analysis.ResultsThe study shows that the AUCs of ROCs on the NLST dataset, LIDC dataset, and IMC dataset are 0.91, 0.86, and 0.95, respectively. The inclusion of clinical features does not significantly improve the model performance. Quantitatively, the summed-up weight on the prediction accuracy of the 10 nodule features extracted by the deep learning algorithm equals to 0.091, while the weight of patient sex, nodule spiculation, and location is 0.031, 0.052, and 0.008, respectively.ConclusionThe convolutional neural network-based model for lung nodule classification could be generalized to multiple datasets containing diverse populations. The addition of three patient clinical features to the nodule features extracted by deep learning does not boost the performance of the model.

COVID-DETECT: A DEEP LEARNING APPROACH FOR CLASSIFICATION OF COVID-19 PNEUMONIA FROM LUNG SEGMENTED CHEST X-RAYS

2020 ◽  
pp. 2150010
Author(s):  
S. Rajkumar ◽  
P. V. Rajaraman ◽  
Haree Shankar Meganathan ◽  
V. Sapthagirivasan ◽  
K. Tejaswinee ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Rapid Screening ◽  
X Rays ◽  
X Ray ◽  
Deep Learning Algorithm ◽  
X Ray Imaging ◽  
Before And After ◽  
Wide Availability ◽  
Novel Coronavirus

The novel coronavirus (COVID-19) was first reported in the Wuhan City of China in 2019 and became a pandemic. The outbreak has caused shocking effects to the people across the globe. It is important to screen a majority of the population in every country and for the respective governments to take appropriate action. There is a need for a rapid screening system to triage and recommend the patients for appropriate treatment. Chest X-ray imaging is one of the potential modalities, which has ample advantages such as wide availability even in the villages, portability, fast data sharing option from the point of capturing to the point of investigation, etc. The aim of the proposed work is to develop a deep learning algorithm for screening COVID-19 cases by leveraging the widely available X-ray imaging. We have built a deep learning Convolutional Neural Network model utilizing a combination of the public domain (open-source COVID-19) and private data (pneumonia and normal cases). The dataset was used before and after the segmentation of the lung region for training and testing. The outcome of the classification after lung segmentation resulted in significant superiority. The average accuracy achieved by the proposed system was 96%. The heat maps incorporated in the system were helpful for our radiologists to cross-verify whether the appropriate features are identified. This system (COVID-Detect) can be used in remote places in the countries affected by COVID-19 for mass screening of suspected cases and suggesting appropriate actions, such as recommending confirmatory tests.

scholarly journals Validation of expert system enhanced deep learning algorithm for automated screening for COVID-Pneumonia on chest X-rays

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Prashant Sadashiv Gidde ◽  
Shyam Sunder Prasad ◽  
Ajay Pratap Singh ◽  
Nitin Bhatheja ◽  
Satyartha Prakash ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Ground Truth ◽  
Training Data ◽  
X Rays ◽  
Rt Pcr ◽  
Deep Learning Algorithm ◽  
High Throughput Detection ◽  
Automated Screening ◽  
Pneumonia Diagnosis

AbstractSARS-CoV2 pandemic exposed the limitations of artificial intelligence based medical imaging systems. Earlier in the pandemic, the absence of sufficient training data prevented effective deep learning (DL) solutions for the diagnosis of COVID-19 based on X-Ray data. Here, addressing the lacunae in existing literature and algorithms with the paucity of initial training data; we describe CovBaseAI, an explainable tool using an ensemble of three DL models and an expert decision system (EDS) for COVID-Pneumonia diagnosis, trained entirely on pre-COVID-19 datasets. The performance and explainability of CovBaseAI was primarily validated on two independent datasets. Firstly, 1401 randomly selected CxR from an Indian quarantine center to assess effectiveness in excluding radiological COVID-Pneumonia requiring higher care. Second, curated dataset; 434 RT-PCR positive cases and 471 non-COVID/Normal historical scans, to assess performance in advanced medical settings. CovBaseAI had an accuracy of 87% with a negative predictive value of 98% in the quarantine-center data. However, sensitivity was 0.66–0.90 taking RT-PCR/radiologist opinion as ground truth. This work provides new insights on the usage of EDS with DL methods and the ability of algorithms to confidently predict COVID-Pneumonia while reinforcing the established learning; that benchmarking based on RT-PCR may not serve as reliable ground truth in radiological diagnosis. Such tools can pave the path for multi-modal high throughput detection of COVID-Pneumonia in screening and referral.

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