Machine-learning classification of texture features of portable chest X-ray accurately classifies COVID-19 lung infection

被引：0
|
作者：
Lal Hussain
Tony Nguyen
Haifang Li
Adeel A. Abbasi
Kashif J. Lone
Zirun Zhao
Mahnoor Zaib
Anne Chen
Tim Q. Duong
机构：
[1] University of Azad Jammu and Kashmir,Department of Computer Science and IT, King Abdullah Campus
[2] University of Azad Jammu and Kashmir,Department of Computer Science and IT, Neelum Campus
[3] Renaissance School of Medicine at Stony Brook University,Department of Radiology
来源：
BioMedical Engineering OnLine | / 19卷
关键词：
Texture; Morphological; Machine learning; Feature extraction; Classification; COVID-19;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
引用
收藏
相关论文
共 50 条
  • [31] Automated Identification of COVID-19 from Chest X-Ray Images Using Machine Learning
    Biswas, Debanshu
    Sahoo, Abhaya Kumar
    IETE JOURNAL OF RESEARCH, 2024, 70 (04) : 3603 - 3613
  • [32] Classification of Chest X-ray Images to Diagnose Covid-19 using Deep Learning Techniques
    Santos Silva, Isabel Heloise
    Barros Negreiros, Ramoni Reus
    Firmino Alves, Andre Luiz
    Gomes Valadares, Dalton Cezane
    Perkusich, Angelo
    WINSYS : PROCEEDINGS OF THE 19TH INTERNATIONAL CONFERENCE ON WIRELESS NETWORKS AND MOBILE SYSTEMS, 2022, : 93 - 100
  • [33] Optimal Synergic Deep Learning for COVID-19 Classification Using Chest X-Ray Images
    Escorcia-Gutierrez, Jose
    Gamarra, Margarita
    Soto-Diaz, Roosvel
    Alsafari, Safa
    Yafoz, Ayman
    Mansour, Romany F.
    CMC-COMPUTERS MATERIALS & CONTINUA, 2023, 75 (03): : 5255 - 5270
  • [34] Validating deep learning inference during chest X-ray classification for COVID-19 screening
    Sadre, Robbie
    Sundaram, Baskaran
    Majumdar, Sharmila
    Ushizima, Daniela
    SCIENTIFIC REPORTS, 2021, 11 (01)
  • [35] Loss of Smell and Taste Can Accurately Predict COVID-19 Infection: A Machine-Learning Approach
    Callejon-Leblic, Maria A.
    Moreno-Luna, Ramon
    Del Cuvillo, Alfonso
    Reyes-Tejero, Isabel M.
    Garcia-Villaran, Miguel A.
    Santos-Pena, Marta
    Maza-Solano, Juan M.
    Martin-Jimenez, Daniel, I
    Palacios-Garcia, Jose M.
    Fernandez-Velez, Carlos
    Gonzalez-Garcia, Jaime
    Sanchez-Calvo, Juan M.
    Solanellas-Soler, Juan
    Sanchez-Gomez, Serafin
    JOURNAL OF CLINICAL MEDICINE, 2021, 10 (04) : 1 - 17
  • [36] Detection of COVID-19 coronavirus infection in chest X-ray images with deep learning methods
    Shchetinin, E. Yu
    COMPUTER OPTICS, 2022, 46 (06) : 963 - +
  • [37] Portable chest X-ray in coronavirus disease-19 (COVID-19): A pictorial review
    Jacobi, Adam
    Chung, Michael
    Bernheim, Adam
    Eber, Corey
    CLINICAL IMAGING, 2020, 64 : 35 - 42
  • [38] Detection of Covid-19 in Chest X-Ray Images Using Percolation Features and Hermite Polynomial Classification
    Roberto, Guilherme F.
    Pereira, Danilo C.
    Martins, Alessandro S.
    Tosta, Thaina A. A.
    Soares, Carlos
    Lumini, Alessandra
    Rozendo, Guilherme B.
    Neves, Leandro A.
    Nascimento, Marcelo Z.
    PROGRESS IN PATTERN RECOGNITION, IMAGE ANALYSIS, COMPUTER VISION, AND APPLICATIONS, CIARP 2023, PT I, 2024, 14469 : 163 - 177
  • [39] Classification of COVID-19 from chest x-ray images using deep features and correlation coefficient
    Kumar, Rahul
    Arora, Ridhi
    Bansal, Vipul
    Sahayasheela, Vinodh J.
    Buckchash, Himanshu
    Imran, Javed
    Narayanan, Narayanan
    Pandian, Ganesh N.
    Raman, Balasubramanian
    MULTIMEDIA TOOLS AND APPLICATIONS, 2022, 81 (19) : 27631 - 27655
  • [40] Classification of COVID-19 from chest x-ray images using deep features and correlation coefficient
    Rahul Kumar
    Ridhi Arora
    Vipul Bansal
    Vinodh J Sahayasheela
    Himanshu Buckchash
    Javed Imran
    Narayanan Narayanan
    Ganesh N Pandian
    Balasubramanian Raman
    Multimedia Tools and Applications, 2022, 81 : 27631 - 27655
12345