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AI-Assisted Tuberculosis Detection and Classification from Chest X-Rays Using a Deep Learning Normalization-Free Network Model

Tuberculosis (TB) is an airborne disease caused by Mycobacterium tuberculosis. It is imperative to detect cases of TB as early as possible because if left untreated, there is a 70% chance of a patient dying within 10 years. The necessity for supplementary tools has increased in mid to low-income cou...

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Autores principales: Acharya, Vasundhara, Dhiman, Gaurav, Prakasha, Krishna, Bahadur, Pranshu, Choraria, Ankit, M, Sushobhitha, J, Sowjanya, Prabhu, Srikanth, Chadaga, Krishnaraj, Viriyasitavat, Wattana, Kautish, Sandeep
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9550434/
https://www.ncbi.nlm.nih.gov/pubmed/36225551
http://dx.doi.org/10.1155/2022/2399428
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author Acharya, Vasundhara
Dhiman, Gaurav
Prakasha, Krishna
Bahadur, Pranshu
Choraria, Ankit
M, Sushobhitha
J, Sowjanya
Prabhu, Srikanth
Chadaga, Krishnaraj
Viriyasitavat, Wattana
Kautish, Sandeep
author_facet Acharya, Vasundhara
Dhiman, Gaurav
Prakasha, Krishna
Bahadur, Pranshu
Choraria, Ankit
M, Sushobhitha
J, Sowjanya
Prabhu, Srikanth
Chadaga, Krishnaraj
Viriyasitavat, Wattana
Kautish, Sandeep
author_sort Acharya, Vasundhara
collection PubMed
description Tuberculosis (TB) is an airborne disease caused by Mycobacterium tuberculosis. It is imperative to detect cases of TB as early as possible because if left untreated, there is a 70% chance of a patient dying within 10 years. The necessity for supplementary tools has increased in mid to low-income countries due to the rise of automation in healthcare sectors. The already limited resources are being heavily allocated towards controlling other dangerous diseases. Modern digital radiography (DR) machines, used for screening chest X-rays of potential TB victims are very practical. Coupled with computer-aided detection (CAD) with the aid of artificial intelligence, radiologists working in this field can really help potential patients. In this study, progressive resizing is introduced for training models to perform automatic inference of TB using chest X-ray images. ImageNet fine-tuned Normalization-Free Networks (NFNets) are trained for classification and the Score-Cam algorithm is utilized to highlight the regions in the chest X-Rays for detailed inference on the diagnosis. The proposed method is engineered to provide accurate diagnostics for both binary and multiclass classification. The models trained with this method have achieved 96.91% accuracy, 99.38% AUC, 91.81% sensitivity, and 98.42% specificity on a multiclass classification dataset. Moreover, models have also achieved top-1 inference metrics of 96% accuracy and 98% AUC for binary classification. The results obtained demonstrate that the proposed method can be used as a secondary decision tool in a clinical setting for assisting radiologists.
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spelling pubmed-95504342022-10-11 AI-Assisted Tuberculosis Detection and Classification from Chest X-Rays Using a Deep Learning Normalization-Free Network Model Acharya, Vasundhara Dhiman, Gaurav Prakasha, Krishna Bahadur, Pranshu Choraria, Ankit M, Sushobhitha J, Sowjanya Prabhu, Srikanth Chadaga, Krishnaraj Viriyasitavat, Wattana Kautish, Sandeep Comput Intell Neurosci Research Article Tuberculosis (TB) is an airborne disease caused by Mycobacterium tuberculosis. It is imperative to detect cases of TB as early as possible because if left untreated, there is a 70% chance of a patient dying within 10 years. The necessity for supplementary tools has increased in mid to low-income countries due to the rise of automation in healthcare sectors. The already limited resources are being heavily allocated towards controlling other dangerous diseases. Modern digital radiography (DR) machines, used for screening chest X-rays of potential TB victims are very practical. Coupled with computer-aided detection (CAD) with the aid of artificial intelligence, radiologists working in this field can really help potential patients. In this study, progressive resizing is introduced for training models to perform automatic inference of TB using chest X-ray images. ImageNet fine-tuned Normalization-Free Networks (NFNets) are trained for classification and the Score-Cam algorithm is utilized to highlight the regions in the chest X-Rays for detailed inference on the diagnosis. The proposed method is engineered to provide accurate diagnostics for both binary and multiclass classification. The models trained with this method have achieved 96.91% accuracy, 99.38% AUC, 91.81% sensitivity, and 98.42% specificity on a multiclass classification dataset. Moreover, models have also achieved top-1 inference metrics of 96% accuracy and 98% AUC for binary classification. The results obtained demonstrate that the proposed method can be used as a secondary decision tool in a clinical setting for assisting radiologists. Hindawi 2022-10-03 /pmc/articles/PMC9550434/ /pubmed/36225551 http://dx.doi.org/10.1155/2022/2399428 Text en Copyright © 2022 Vasundhara Acharya et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Acharya, Vasundhara
Dhiman, Gaurav
Prakasha, Krishna
Bahadur, Pranshu
Choraria, Ankit
M, Sushobhitha
J, Sowjanya
Prabhu, Srikanth
Chadaga, Krishnaraj
Viriyasitavat, Wattana
Kautish, Sandeep
AI-Assisted Tuberculosis Detection and Classification from Chest X-Rays Using a Deep Learning Normalization-Free Network Model
title AI-Assisted Tuberculosis Detection and Classification from Chest X-Rays Using a Deep Learning Normalization-Free Network Model
title_full AI-Assisted Tuberculosis Detection and Classification from Chest X-Rays Using a Deep Learning Normalization-Free Network Model
title_fullStr AI-Assisted Tuberculosis Detection and Classification from Chest X-Rays Using a Deep Learning Normalization-Free Network Model
title_full_unstemmed AI-Assisted Tuberculosis Detection and Classification from Chest X-Rays Using a Deep Learning Normalization-Free Network Model
title_short AI-Assisted Tuberculosis Detection and Classification from Chest X-Rays Using a Deep Learning Normalization-Free Network Model
title_sort ai-assisted tuberculosis detection and classification from chest x-rays using a deep learning normalization-free network model
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9550434/
https://www.ncbi.nlm.nih.gov/pubmed/36225551
http://dx.doi.org/10.1155/2022/2399428
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