Clustering COVID-19 Lung Scans

• URL: http://arxiv.org/abs/2009.09899v2
• Date: Wed, 1 Dec 2021 06:21:58 GMT
• Title: Clustering COVID-19 Lung Scans
• Authors: Jacob Householder, Andrew Householder, John Paul Gomez-Reed, Fredrick Park, Shuai Zhang
• Abstract summary: Group applied unsupervised clustering techniques to explore a dataset of lungscans of COVID-19 infected, Viral Pneumonia infected, and healthy individuals. Our methodology explores the potential that unsupervised clustering algorithms have to reveal important hidden differences between COVID-19 and other respiratory illnesses.
• Score: 3.5447971809011882
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the ongoing COVID-19 pandemic, understanding the characteristics of the virus has become an important and challenging task in the scientific community. While tests do exist for COVID-19, the goal of our research is to explore other methods of identifying infected individuals. Our group applied unsupervised clustering techniques to explore a dataset of lungscans of COVID-19 infected, Viral Pneumonia infected, and healthy individuals. This is an important area to explore as COVID-19 is a novel disease that is currently being studied in detail. Our methodology explores the potential that unsupervised clustering algorithms have to reveal important hidden differences between COVID-19 and other respiratory illnesses. Our experiments use: Principal Component Analysis (PCA), K-Means++ (KM++) and the recently developed Robust Continuous Clustering algorithm (RCC). We evaluate the performance of KM++ and RCC in clustering COVID-19 lung scans using the Adjusted Mutual Information (AMI) score.

Related papers

• CoVScreen: Pitfalls and recommendations for screening COVID-19 using Chest X-rays [1.0878040851637998]
  The novel coronavirus (COVID-19), a highly infectious respiratory disease caused by the SARS-CoV-2 has emerged as an unprecedented healthcare crisis. Early screening and diagnosis of symptomatic patients plays crucial role in isolation of patient to help stop community transmission. Due to its accessibility, availability, lower-cost, ease of sanitisation, and portable setup, chest X-Ray imaging can serve as an effective screening and diagnostic tool.
  arXiv  Detail & Related papers  (2024-05-13T12:03:15Z)
• COVID-19 Detection Based on Blood Test Parameters using Various Artificial Intelligence Methods [1.2408125305560274]
  In 2019, the world faced a new challenge: a COVID-19 disease caused by the novel coronavirus, SARS-CoV-2. This study aimed to differentiate COVID-19 patients from others using self-categorizing classifiers and employing various AI methods.
  arXiv  Detail & Related papers  (2024-04-02T22:49:25Z)
• The pitfalls of using open data to develop deep learning solutions for COVID-19 detection in chest X-rays [64.02097860085202]
  Deep learning models have been developed to identify COVID-19 from chest X-rays. Results have been exceptional when training and testing on open-source data. Data analysis and model evaluations show that the popular open-source dataset COVIDx is not representative of the real clinical problem.
  arXiv  Detail & Related papers  (2021-09-14T10:59:11Z)
• Exploiting Shared Knowledge from Non-COVID Lesions for Annotation-Efficient COVID-19 CT Lung Infection Segmentation [10.667692828593125]
  We propose a relation-driven collaborative learning model for COVID-19 lung infection segmentation. We exploit shared knowledge between COVID and non-COVID lesions to regularize the relation consistency between extracted features. Our method achieves superior segmentation performance compared with existing methods in the absence of sufficient high-quality COVID-19 annotations.
  arXiv  Detail & Related papers  (2020-12-31T11:40:29Z)
• Studying the Similarity of COVID-19 Sounds based on Correlation Analysis of MFCC [1.9659095632676098]
  We illustrate the importance of speech signal processing in the extraction of the Mel-Frequency Cepstral Coefficients (MFCCs) of the COVID-19 and non-COVID-19 samples. Our results show high similarity in MFCCs between different COVID-19 cough and breathing sounds, while MFCC of voice is more robust between COVID-19 and non-COVID-19 samples.
  arXiv  Detail & Related papers  (2020-10-17T11:38:05Z)
• COVIDNet-CT: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest CT Images [75.74756992992147]
  We introduce COVIDNet-CT, a deep convolutional neural network architecture that is tailored for detection of COVID-19 cases from chest CT images. We also introduce COVIDx-CT, a benchmark CT image dataset derived from CT imaging data collected by the China National Center for Bioinformation.
  arXiv  Detail & Related papers  (2020-09-08T15:49:55Z)
• A New Screening Method for COVID-19 based on Ocular Feature Recognition by Machine Learning Tools [66.20818586629278]
  Coronavirus disease 2019 (COVID-19) has affected several million people. New screening method of analyzing the eye-region images, captured by common CCD and CMOS cameras, could reliably make a rapid risk screening of COVID-19.
  arXiv  Detail & Related papers  (2020-09-04T00:50:27Z)
• Understanding the temporal evolution of COVID-19 research through machine learning and natural language processing [66.63200823918429]
  The outbreak of the novel coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been continuously affecting human lives and communities around the world. We used multiple data sources, i.e., PubMed and ArXiv, and built several machine learning models to characterize the landscape of current COVID-19 research. Our findings confirm the types of research available in PubMed and ArXiv differ significantly, with the former exhibiting greater diversity in terms of COVID-19 related issues.
  arXiv  Detail & Related papers  (2020-07-22T18:02:39Z)
• COVID-DA: Deep Domain Adaptation from Typical Pneumonia to COVID-19 [92.4955073477381]
  The outbreak of novel coronavirus disease 2019 (COVID-19) has already infected millions of people and is still rapidly spreading all over the globe. Deep learning has been used recently as effective computer-aided means to improve diagnostic efficiency. We propose a new deep domain adaptation method for COVID-19 diagnosis, namely COVID-DA.
  arXiv  Detail & Related papers  (2020-04-30T03:13:40Z)
• Target specific mining of COVID-19 scholarly articles using one-class approach [3.4935179780034247]
  This paper aims to extract the activity and trends of corona-virus related research articles using machine learning approaches. The k-means clustering algorithm, followed by parallel OCSVMs, outperforms other methods for both original and reduced feature space.
  arXiv  Detail & Related papers  (2020-04-24T12:39:54Z)
• COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Images [93.0013343535411]
  We introduce COVID-Net, a deep convolutional neural network design tailored for the detection of COVID-19 cases from chest X-ray (CXR) images. To the best of the authors' knowledge, COVID-Net is one of the first open source network designs for COVID-19 detection from CXR images. We also introduce COVIDx, an open access benchmark dataset that we generated comprising of 13,975 CXR images across 13,870 patient patient cases.
  arXiv  Detail & Related papers  (2020-03-22T12:26:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.