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Unsupervised chest X-ray opacity classification using minimal deep features

Che Azemin, Mohd Zulfaezal and Mohd Tamrin, Mohd Izzuddin and Md. Ali, Mohd. Adli and Jamaludin, Iqbal (2022) Unsupervised chest X-ray opacity classification using minimal deep features. International Journal of Advanced Computer Science and Applications, 13 (3). pp. 259-262. ISSN 2158-107X E-ISSN 2156-5570

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Data privacy has been a concern in medical imaging research. One important step to minimize the sharing of patient’s information is by limiting the use of original images in the workflow. This research aimed to use minimal deep learning features in detecting anomaly in chest X-ray (CXR) images. A total of 3,504 CXRs were processed using a pre-trained deep learning convolutional neural network to output ten discriminatory features which were then used in the k-mean algorithm to find underlying similarities between the features for further clustering. Two clusters were set to distinguish between “Opacity” and “Normal” CXRs with the accuracy, sensitivity, specificity, and positive predictive value of 80.9%, 86.6%, 71.5% and 83.1%, respectively. With only ten features required to build the unsupervised model, this would pave the way for future federated learning research where actual CXRs can remain distributed over multiple centers without sacrificing the anonymity of the patients.

Item Type: Article (Journal)
Uncontrolled Keywords: Unsupervised classification; minimal deep features; convolution neural network; chest x-ray; airspace opacity
Subjects: R Medicine > RC Internal medicine > RC731 Specialties of Internal Medicine-Diseases of The Respiratory System
T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices > TK7885 Computer engineering
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): Kulliyyah of Allied Health Sciences
Kulliyyah of Information and Communication Technology
Kulliyyah of Information and Communication Technology

Kulliyyah of Science
Depositing User: Mohd Izzuddin Mohd Tamrin
Date Deposited: 31 Mar 2022 10:51
Last Modified: 14 Jun 2022 12:33
URI: http://irep.iium.edu.my/id/eprint/97427

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