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Amith Khandakar
Researcher at Qatar University
Publications - Â 121
Citations - Â 4021
Amith Khandakar is an academic researcher from Qatar University. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 15, co-authored 76 publications receiving 1027 citations. Previous affiliations of Amith Khandakar include National University of Malaysia & North South University.
Papers
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Journal ArticleDOI
Can AI Help in Screening Viral and COVID-19 Pneumonia?
Muhammad E. H. Chowdhury,Tawsifur Rahman,Amith Khandakar,Rashid Mazhar,Muhammad Abdul Kadir,Zaid Bin Mahbub,Khandakar Reajul Islam,Muhammad Salman Khan,Atif Iqbal,Nasser Al Emadi,Mamun Bin Ibne Reaz,Mohammad Tariqul Islam +11 more
TL;DR: The aim of this paper is to propose a robust technique for automatic detection of COVID-19 pneumonia from digital chest X-ray images applying pre-trained deep-learning algorithms while maximizing the detection accuracy.
Journal ArticleDOI
Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images.
Tawsifur Rahman,Amith Khandakar,Yazan Qiblawey,Anas Tahir,Serkan Kiranyaz,Saad Bin Abul Kashem,Mohammad Tariqul Islam,Somaya Al Maadeed,Susu M. Zughaier,Muhammad Salman Khan,Muhammad E. H. Chowdhury +10 more
TL;DR: In this article, the effect of image enhancement and lung segmentation of a large dataset in COVID-19 detection was not reported in the literature; however, the proposed approach with very reliable and comparable performance will boost the fast and robust detection of coronavirus disease using chest X-ray images.
Posted Content
Exploring the Effect of Image Enhancement Techniques on COVID-19 Detection using Chest X-rays Images
Tawsifur Rahman,Amith Khandakar,Yazan Qiblawey,Anas Tahir,Serkan Kiranyaz,Saad Bin Abul Kashem,Mohammad Tariqul Islam,Somaya Al Maadeed,Susu M. Zughaier,Muhammad Salman Khan,Muhammad E. H. Chowdhury +10 more
TL;DR: An approach with very reliable and comparable performance will boost the fast and robust COVID-19 detection using chest X-ray images and the reliability of network performance is significantly improved for the segmented lung images, which was observed using the visualization technique.
Journal ArticleDOI
Transfer Learning with Deep Convolutional Neural Network (CNN) for Pneumonia Detection Using Chest X-ray
Tawsifur Rahman,Muhammad E. H. Chowdhury,Amith Khandakar,Khandakar Reajul Islam,Khandaker F. Islam,Zaid Bin Mahbub,Muhammad Abdul Kadir,Saad Bin Abul Kashem +7 more
TL;DR: The proposed study can be useful in faster-diagnosing pneumonia by the radiologist and can help in the fast airport screening of pneumonia patients.
Journal ArticleDOI
Reliable Tuberculosis Detection Using Chest X-Ray With Deep Learning, Segmentation and Visualization
Tawsifur Rahman,Amith Khandakar,Muhammad Abdul Kadir,Khandaker Rejaul Islam,Khandakar F. Islam,Rashid Mazhar,Tahir Hamid,Mohammad Tariqul Islam,Saad Bin Abul Kashem,Zaid Bin Mahbub,Mohamed Arselene Ayari,Muhammad E. H. Chowdhury +11 more
TL;DR: This work has detected TB reliably from the chest X-ray images using image pre-processing, data augmentation, image segmentation, and deep-learning classification techniques and confirmed that CNN learns dominantly from the segmented lung regions that resulted in higher detection accuracy.