CoronaNet: A Novel Deep Learning Model for COVID-19 Detection in CT Scans

Coronavirus disease (COVID-19) is currently the cause of a global pandemic that is affecting millions of people around the world. Inadequate testing resources have resulted in several people going undiagnosed and consequently untreated; however, using computerized tomography (CT) scans for diagnosis is an alternative to bypass this limitation. Unfortunately, CT scan analysis is time-consuming and labor intensive and rendering is generally infeasible in most diagnosis situations. In order to alleviate this problem, previous studies have utilized multiple deep learning techniques to analyze biomedical images such as CT scans. Specifically, convolutional neural networks (CNNs) have been shown to provide medical diagnosis with a high degree of accuracy. A common issue in the training of CNNs for biomedical applications is the requirement of large datasets. In this paper, we propose the use of affine transformations to artificially magnify the size of our dataset. Additionally, we propose the use of the Laplace filter to increase feature detection in CT scan analysis. We then feed the preprocessed images to a novel deep CNN structure: CoronaNet. We find that the use of the Laplace filter significantly increases the performance of CoronaNet across all metrics. Additionally, we find that affine transformations successfully magnify the dataset without resulting in high degrees of overfitting. Specifically, we achieved an accuracy of 92% and an F1 of 0.8735. Our novel research describes the potential of the Laplace filter to significantly increase deep CNN performance in biomedical applications such as COVID-19 diagnosis.

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A Novel Elastic Wrap for Syndesmotic Reduction with Post-Operative CT Scan Analysis

Foot & Ankle Orthopaedics ◽

10.1177/2473011419s00374 ◽

2019 ◽

Vol 4 (4) ◽

pp. 2473011419S0037

Author(s):

Daniel R. Schlatterer ◽

Chet Despande ◽

Aaron Morgenstein

Keyword(s):

Clinical Practice ◽

Ct Scan ◽

Screw Fixation ◽

Ct Scans ◽

Anatomic Reduction ◽

Ankle Trauma ◽

Common Clinical Practice ◽

Ct Scan Analysis ◽

Uniform Reduction

Category: Ankle, Trauma Introduction/Purpose: Syndesmosis malreductions occur in up to 50% of patients. Several studies concluded that the position of the reduction tines of the periarticular clamp determines the final fibular position. The purpose of this study was to determine if an elastic wrap would provide a more uniform reduction force resulting in an anatomic syndesmosis reduction. We hypothesized that the force applied to the ankle by an elastic wrap would be relatively low and uniform circumferentially around the ankle medially and laterally. Furthermore we thought the ankle wrap would negate the dependency of clamp tine placement and circumferentialy reduce the syndesmosis perfectly. In this series Syndesmotic injuries were treated with the wrap for reduction, screw fixation and post-operative CT scan verification. Methods: Syndesmosis malreductions occur in up to 50% of patients. Several studies concluded that the position of the reduction tines of the periarticular clamp determines the final fibular position. The purpose of this study was to determine if an elastic wrap would provide a more uniform reduction force resulting in an anatomic syndesmosis reduction. We hypothesized that the force applied to the ankle by an elastic wrap would be relatively low and uniform circumferentially around the ankle medially and laterally. Furthermore we thought the ankle wrap would negate the dependency of clamp tine placement and circumferentialy reduce the syndesmosis perfectly. In this series Syndesmotic injuries were treated with the wrap for reduction, screw fixation and post-operative CT scan verification. Results: In a grossly unstable cadaver ankle model the ankle wrap achieved a perfect reduction every time it was trialed. The pressure film component of this study confirmed a uniform reduction force circumferentially at the ankle under the ankle wrap device of 5-9 pounds per square inch. Post-operative CT scans in 5 cases confirmed anatomic reduction of the syndesmosis in those cases treated surgically with the wrap and screw fixation. Conclusion: Malreduction of the syndesmosis can be avoided by using an elastic wrap instead of the standard peri-articular clamp in common clinical practice today.

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Imperial Needs, Imperial Methods: Chimú Ceramic Manufacturing Process Through CT Scan Analysis of Stirrup-Spout Bottles

Latin American Antiquity ◽

10.7183/1045-6635.27.2.238 ◽

2016 ◽

Vol 27 (2) ◽

pp. 238-256

Author(s):

Valentine Wauters

Keyword(s):

Computed Tomography ◽

Ct Scan ◽

Manufacturing Process ◽

Ceramic Production ◽

Ct Scans ◽

Manufacturing Processes ◽

Ceramic Assemblage ◽

Ceramic Manufacturing ◽

Industrial Level ◽

Ct Scan Analysis

The stirrup-spout bottle is one of the most representative forms in the Chimú (A.D. 900-1470) ceramic repertoire. I discuss the ceramic assemblage of this coastal culture and describes more precisely the various manufacturing processes of the stirrup-spout bottle. Although molds used to produce these complex vessels are known today, only little information has been published on the various stages involved in their manufacture. My purpose is to contribute to this research using medical imaging computed tomography (CT) scans of intact stirrup-spout vessels. Based on my findings, I propose that changes in the construction of these vessels correlated with a transition in ceramic production to a semi-industrial level during the time of the Chimú Empire.

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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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Detection of COVID-19 from CT scan images: A spiking neural network-based approach

Neural Computing and Applications ◽

10.1007/s00521-021-05910-1 ◽

2021 ◽

Author(s):

Avishek Garain ◽

Arpan Basu ◽

Fabio Giampaolo ◽

Juan D. Velasquez ◽

Ram Sarkar

Keyword(s):

Deep Learning ◽

Ct Scan ◽

Graphics Processing Units ◽

Early Stage ◽

Medical Image Analysis ◽

Global Pandemic ◽

Radiological Images ◽

Chest X Ray ◽

Computer Based ◽

Graphics Processing

AbstractThe outbreak of a global pandemic called coronavirus has created unprecedented circumstances resulting into a large number of deaths and risk of community spreading throughout the world. Desperate times have called for desperate measures to detect the disease at an early stage via various medically proven methods like chest computed tomography (CT) scan, chest X-Ray, etc., in order to prevent the virus from spreading across the community. Developing deep learning models for analysing these kinds of radiological images is a well-known methodology in the domain of computer based medical image analysis. However, doing the same by mimicking the biological models and leveraging the newly developed neuromorphic computing chips might be more economical. These chips have been shown to be more powerful and are more efficient than conventional central and graphics processing units. Additionally, these chips facilitate the implementation of spiking neural networks (SNNs) in real-world scenarios. To this end, in this work, we have tried to simulate the SNNs using various deep learning libraries. We have applied them for the classification of chest CT scan images into COVID and non-COVID classes. Our approach has achieved very high F1 score of 0.99 for the potential-based model and outperforms many state-of-the-art models. The working code associated with our present work can be found here.

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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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A Deep Learning Approach for the Automatic Identification of the Left Atrium Within CT Scans

2019 Design of Medical Devices Conference ◽

10.1115/dmd2019-3282 ◽

2019 ◽

Author(s):

Alex Deakyne ◽

Erik Gaasedelen ◽

Paul A. Iaizzo

Keyword(s):

Deep Learning ◽

Left Atrium ◽

Feature Detection ◽

Ct Scans ◽

Automatic Identification ◽

Learning Approach ◽

Anatomical Features ◽

Major Development ◽

Bounding Boxes ◽

Single Neural Network

Recent advancements in deep learning have led to the possibility of increased performance in computer vision tools. A major development has been the usage of Convolutional Neural Networks (CNN) for automatically detecting features within a given image. Architectures such as YOLO1 have obtained incredibly high performances for the real-time detection of every-day objects within images. However to date, there have been few reports of deep learning applied to detect anatomical features within CT scans; especially those within the cardiovascular space. We propose here an automatic anatomical feature detection pipeline for identifying the features of the left atrium using a CNN. Slices of CT scans were fed into a single neural network which predicted the four bounding box coordinates that encapsulate the left atrium. The network can be optimized end-to-end and generate predictions at great speed, achieving a validation smooth L1 loss of 11.95 when predicting the left atrial bounding boxes.

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Integrating deep learning CT-scan model, biological and clinical variables to predict severity of COVID-19 patients

Nature Communications ◽

10.1038/s41467-020-20657-4 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Nathalie Lassau ◽

Samy Ammari ◽

Emilie Chouzenoux ◽

Hugo Gortais ◽

Paul Herent ◽

...

Keyword(s):

Deep Learning ◽

Ct Scan ◽

Learning Model ◽

Ct Scans ◽

Clinical Variables ◽

Biological Variables ◽

Severity Scores ◽

Scan Data ◽

Deep Learning Model ◽

Chest Ct Scan

AbstractThe SARS-COV-2 pandemic has put pressure on intensive care units, so that identifying predictors of disease severity is a priority. We collect 58 clinical and biological variables, and chest CT scan data, from 1003 coronavirus-infected patients from two French hospitals. We train a deep learning model based on CT scans to predict severity. We then construct the multimodal AI-severity score that includes 5 clinical and biological variables (age, sex, oxygenation, urea, platelet) in addition to the deep learning model. We show that neural network analysis of CT-scans brings unique prognosis information, although it is correlated with other markers of severity (oxygenation, LDH, and CRP) explaining the measurable but limited 0.03 increase of AUC obtained when adding CT-scan information to clinical variables. Here, we show that when comparing AI-severity with 11 existing severity scores, we find significantly improved prognosis performance; AI-severity can therefore rapidly become a reference scoring approach.

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A deep learning method for automatic segmentation of the bony orbit in MRI and CT images

Scientific Reports ◽

10.1038/s41598-021-93227-3 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Jared Hamwood ◽

Beat Schmutz ◽

Michael J. Collins ◽

Mark C. Allenby ◽

David Alonso-Caneiro

Keyword(s):

Deep Learning ◽

Human Performance ◽

Automatic Segmentation ◽

Ct Images ◽

Human Observer ◽

In Series ◽

Magnetic Resonance Imaging Mri ◽

High Degree ◽

Orbital Region ◽

Ct And Mri

AbstractThis paper proposes a fully automatic method to segment the inner boundary of the bony orbit in two different image modalities: magnetic resonance imaging (MRI) and computed tomography (CT). The method, based on a deep learning architecture, uses two fully convolutional neural networks in series followed by a graph-search method to generate a boundary for the orbit. When compared to human performance for segmentation of both CT and MRI data, the proposed method achieves high Dice coefficients on both orbit and background, with scores of 0.813 and 0.975 in CT images and 0.930 and 0.995 in MRI images, showing a high degree of agreement with a manual segmentation by a human expert. Given the volumetric characteristics of these imaging modalities and the complexity and time-consuming nature of the segmentation of the orbital region in the human skull, it is often impractical to manually segment these images. Thus, the proposed method provides a valid clinical and research tool that performs similarly to the human observer.

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