Recognition of COVID-19 from CT Scans Using Two-Stage Deep-Learning-Based Approach: CNR-IEMN

Since the appearance of the COVID-19 pandemic (at the end of 2019, Wuhan, China), the recognition of COVID-19 with medical imaging has become an active research topic for the machine learning and computer vision community. This paper is based on the results obtained from the 2021 COVID-19 SPGC challenge, which aims to classify volumetric CT scans into normal, COVID-19, or community-acquired pneumonia (Cap) classes. To this end, we proposed a deep-learning-based approach (CNR-IEMN) that consists of two main stages. In the first stage, we trained four deep learning architectures with a multi-tasks strategy for slice-level classification. In the second stage, we used the previously trained models with an XG-boost classifier to classify the whole CT scan into normal, COVID-19, or Cap classes. Our approach achieved a good result on the validation set, with an overall accuracy of 87.75% and 96.36%, 52.63%, and 95.83% sensitivities for COVID-19, Cap, and normal, respectively. On the other hand, our approach achieved fifth place on the three test datasets of SPGC in the COVID-19 challenge, where our approach achieved the best result for COVID-19 sensitivity. In addition, our approach achieved second place on two of the three testing sets.

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Integrated and Configurable Voice Activation and Speaker Verification System for a Robotic Exoskeleton Glove

Volume 10: 44th Mechanisms and Robotics Conference (MR) ◽

10.1115/detc2020-22365 ◽

2020 ◽

Author(s):

Yunfei Guo ◽

Wenda Xu ◽

Sarthak Pradhan ◽

Cesar Bravo ◽

Pinhas Ben-Tzvi

Keyword(s):

Deep Learning ◽

Speaker Verification ◽

Voice Recognition ◽

Research Topic ◽

Sufficient Accuracy ◽

Computing Power ◽

Robotic Exoskeleton ◽

Verification System ◽

Machine Interface ◽

Active Research

Abstract Efficient human-machine interface (HMI) for exoskeletons remains an active research topic, where sample methods have been proposed including using computer vision, EEG (electroencephalogram), and voice recognition. However, some of these methods lack sufficient accuracy, security, and portability. This paper proposes a HMI referred as integrated trigger-word configurable voice activation and speaker verification system (CVASV). The CVASV system is designed for embedded systems with limited computing power that can be applied to any exoskeleton platform. The CVASV system consists of two main sections, including an API based voice activation section and a deep learning based text-independent voice verification section. These two sections are combined into a system that allows the user to configure the activation trigger-word and verify the user’s command in real-time.

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ai-corona: Radiologist-Assistant Deep Learning Framework for COVID-19 Diagnosis in Chest CT Scans

10.1101/2020.05.04.20082081 ◽

2020 ◽

Author(s):

M. Yousefzadeh ◽

P. Esfahanian ◽

S. M. S. Movahed ◽

S. Gorgin ◽

R. Lashgari ◽

...

Keyword(s):

Deep Learning ◽

Ct Scan ◽

Chest Ct ◽

The Other ◽

Ct Scans ◽

Lower Sensitivity ◽

Imaging Features ◽

Learning Framework ◽

Experienced Radiologist ◽

Chest Ct Scan

AbstractBackgroundWith the global outbreak of COVID-19 epidemic since early 2020, there has been considerable attention on CT-based diagnosis as an effective and reliable method. Recently, the advent of deep learning in medical diagnosis has been well proven. Convolutional Neural Networks (CNN) can be used to detect the COVID-19 infection imaging features in a chest CT scan. We introduce ai-corona, a radiologist-assistant deep learning framework for COVID-19 infection diagnosis using the chest CT scans.MethodOur dataset comprises 2121 cases of axial spiral chest CT scans in three classes; COVID-19 abnormal, non COVID-19 abnormal, and normal, from which 1764 cases were used for training and 357 cases for validation. The training set was annotated using the reports of two experienced radiologists. The COVID-19 abnormal class validation set was annotated using the general consensus of a collective of criteria that indicate COVID-19 infection. Moreover, the validation sets for the non COVID-19 abnormal and the normal classes were annotated by a different experienced radiologist. ai-corona constitutes a CNN-based feature extractor conjoined with an average pooling and a fully-connected layer to classify a given chest CT scan into the three aforementioned classes.ResultsWe compare the diagnosis performance of ai-corona, radiologists, and model-assisted radiologists for six combinations of distinguishing between the three mentioned classes, including COVID-19 abnormal vs. others, COVID-19 abnormal vs. normal, COVID-19 abnormal vs. non COVID-19 abnormal, non COVID-19 abnormal vs. others, normal vs. others, and normal vs. abnormal. ai-corona achieves an AUC score of 0.989 (95% CI: 0.984, 0.994), 0.997 (95% CI: 0.995, 0.999), 0.986 (95% CI: 0.981, 0.991), 0.959 (95% CI: 0.944, 0.974), 0.978 (95% CI: 0.968, 0.988), and 0.961 (95% CI: 0.951, 0.971) in each combination, respectively. By employing Bayesian statistics to calculate the accuracies at a 95% confidence interval, ai-corona surpasses the radiologists in distinguishing between the COVID-19 abnormal class and the other two classes (especially the non COVID-19 abnormal class). Our results show that radiologists’ diagnosis performance improves when incorporating ai-corona’s prediction. In addition, we also show that RT-PCR’s diagnosis has a much lower sensitivity compared to all the other methods.Conclusionai-corona is a radiologist-assistant deep learning framework for fast and accurate COVID-19 diagnosis in chest CT scans. Our results ascertain that our framework, as a reliable detection tool, also improves experts’ diagnosis performance and helps especially in diagnosing non-typical COVID-19 cases or non COVID-19 abnormal cases that manifest COVID-19 imaging features in chest CT scan. Our framework is available at: ai-corona.com

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O02 THE ROLE OF DEEP LEARNING IN PREDICTING COMPLEXITY AND COMPLICATIONS IN ABDOMINAL WALL RECONSTRUCTION

British Journal of Surgery ◽

10.1093/bjs/znab396.001 ◽

2021 ◽

Vol 108 (Supplement_8) ◽

Author(s):

Sharbel Elhage ◽

Sullivan Ayuso ◽

Yizi Zhang ◽

Eva Deerenberg ◽

Vedra Augenstein ◽

...

Keyword(s):

Deep Learning ◽

Abdominal Wall Reconstruction ◽

Management Tool ◽

Ct Scans ◽

Pulmonary Failure ◽

Image Characteristics ◽

Tertiary Center ◽

Surgical Complexity ◽

Operative Planning ◽

Validation Set

Abstract Aim The aim of our study was to evaluate the utility of image-based deep learning models (DLMs) to predict surgical complexity and postoperative outcomes in patients undergoing AWR. Material and Methods A prospective, tertiary center, hernia database was queried for open AWR patients with adequate pre-operative CT-scans. An 8-layer convolutional neural network (CNN) analyzed image characteristics in Python utilizing the open source Tensorflow© and OpenCV frameworks. Images were analyzed and batched into a training set (80%) and validation set (20%) used to analyze the model output, which was blinded to the CNN until testing. DLMs were run to assess surgical complexity based on need for component separation, surgical site infection (SSI), and pulmonary failure. The surgical complexity DLM was validated by comparison to 6 expert AWR surgeons. Results In total, 369 patient CT scans were utilized. The surgical complexity DLM performed well (ROC=0.744;p<0.0001), and when compared to surgeon prediction on the validation set, performed better with an accuracy of 81.3% compared to 65.0% (p < 0.0001). The SSI DLM was successful with an ROC of 0.898 (p < 0.0001). The DLM for predicting pulmonary failure was less effective with an ROC of 0.545 (p = 0.03). Conclusions DLMs were able to successfully predict surgical complexity and were more accurate than expert surgeons using objective, pre-operative imaging. DLMs were also successful in predicting SSI. This breakthrough may allow for enhanced pre-operative planning, including resource utilization and possible need for tertiary center referral. AI appears to be an exciting new management tool in complex AWR.

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nnTwo-Stage Deep Learning Framework for Discrimination between COVID-19 and Community-Acquired Pneumonia from Chest CT scans

Pattern Recognition Letters ◽

10.1016/j.patrec.2021.10.027 ◽

2021 ◽

Author(s):

Mohamed Abdel-Basset ◽

Hossam Hawash ◽

Nour Moustafa ◽

Osama M. Elkomy

Keyword(s):

Deep Learning ◽

Community Acquired Pneumonia ◽

Chest Ct ◽

Ct Scans ◽

Learning Framework

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From community-acquired pneumonia to COVID-19: a deep learning–based method for quantitative analysis of COVID-19 on thick-section CT scans

European Radiology ◽

10.1007/s00330-020-07042-x ◽

2020 ◽

Vol 30 (12) ◽

pp. 6828-6837 ◽

Cited By ~ 5

Author(s):

Zhang Li ◽

Zheng Zhong ◽

Yang Li ◽

Tianyu Zhang ◽

Liangxin Gao ◽

...

Keyword(s):

Deep Learning ◽

Quantitative Analysis ◽

Community Acquired Pneumonia ◽

Ct Scans ◽

Thick Section

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Lung Segmentation and Characterization in COVID-19 Patients for Assessing Pulmonary Thromboembolism: An Approach Based on Deep Learning and Radiomics

Electronics ◽

10.3390/electronics10202475 ◽

2021 ◽

Vol 10 (20) ◽

pp. 2475

Author(s):

Vitoantonio Bevilacqua ◽

Nicola Altini ◽

Berardino Prencipe ◽

Antonio Brunetti ◽

Laura Villani ◽

...

Keyword(s):

Deep Learning ◽

Pulmonary Thromboembolism ◽

Ct Scans ◽

Dice Coefficient ◽

Training Set ◽

Preliminary Study ◽

Validation Set ◽

Segmentation Task ◽

The Relationship

The COVID-19 pandemic is inevitably changing the world in a dramatic way, and the role of computed tomography (CT) scans can be pivotal for the prognosis of COVID-19 patients. Since the start of the pandemic, great care has been given to the relationship between interstitial pneumonia caused by the infection and the onset of thromboembolic phenomena. In this preliminary study, we collected n = 20 CT scans from the Polyclinic of Bari, all from patients positive with COVID-19, nine of which developed pulmonary thromboembolism (PTE). For eight CT scans, we obtained masks of the lesions caused by the infection, annotated by expert radiologists; whereas for the other four CT scans, we obtained masks of the lungs (including both healthy parenchyma and lesions). We developed a deep learning-based segmentation model that utilizes convolutional neural networks (CNNs) in order to accurately segment the lung and lesions. By considering the images from publicly available datasets, we also realized a training set composed of 32 CT scans and a validation set of 10 CT scans. The results obtained from the segmentation task are promising, allowing to reach a Dice coefficient higher than 97%, posing the basis for analysis concerning the assessment of PTE onset. We characterized the segmented region in order to individuate radiomic features that can be useful for the prognosis of PTE. Out of 919 extracted radiomic features, we found that 109 present different distributions according to the Mann–Whitney U test with corrected p-values less than 0.01. Lastly, nine uncorrelated features were retained that can be exploited to realize a prognostic signature.

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Geocomputing, New Technologies and Historical Analysis: Tools for a Changing Planet

Proceedings ◽

10.3390/proceedings2019030009 ◽

2019 ◽

Vol 30 (1) ◽

pp. 9

Author(s):

Sebastiano Trevisani

Keyword(s):

Quantitative Analysis ◽

Philosophy Of Science ◽

New Technologies ◽

Historical Analysis ◽

Holistic Approach ◽

Research Topic ◽

The Other ◽

Historical Records ◽

The Earth ◽

The Relationship

Modern Earth Scientists need also to interact with other disciplines, apparently far from the Earth Sciences and Engineering. Disciplines related to history and philosophy of science are emblematic from this perspective. From one side, the quantitative analysis of information extracted from historical records (documents, maps, paintings, etc.) represents an exciting research topic, requiring a truly holistic approach. On the other side, epistemological and philosophy of science considerations on the relationship between geoscience and society in history are of fundamental importance for understanding past, present and future geosphere-anthroposphere interlinked dynamics.

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A Generalization Performance Study Using Deep Learning Networks in Embedded Systems

Sensors ◽

10.3390/s21041031 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1031

Author(s):

Joseba Gorospe ◽

Rubén Mulero ◽

Olatz Arbelaitz ◽

Javier Muguerza ◽

Miguel Ángel Antón

Keyword(s):

Deep Learning ◽

Embedded Systems ◽

Embedded System ◽

General Purpose ◽

Learning Networks ◽

Performance Study ◽

Learning Techniques ◽

Wide Range ◽

Learning Architectures

Deep learning techniques are being increasingly used in the scientific community as a consequence of the high computational capacity of current systems and the increase in the amount of data available as a result of the digitalisation of society in general and the industrial world in particular. In addition, the immersion of the field of edge computing, which focuses on integrating artificial intelligence as close as possible to the client, makes it possible to implement systems that act in real time without the need to transfer all of the data to centralised servers. The combination of these two concepts can lead to systems with the capacity to make correct decisions and act based on them immediately and in situ. Despite this, the low capacity of embedded systems greatly hinders this integration, so the possibility of being able to integrate them into a wide range of micro-controllers can be a great advantage. This paper contributes with the generation of an environment based on Mbed OS and TensorFlow Lite to be embedded in any general purpose embedded system, allowing the introduction of deep learning architectures. The experiments herein prove that the proposed system is competitive if compared to other commercial systems.

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