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Reliable Sarcoidosis Detection Using Chest X-rays with EfficientNets and Stain-Normalization Techniques
Sarcoidosis is frequently misdiagnosed as tuberculosis (TB) and consequently mistreated due to inherent limitations in radiological presentations. Clinically, to distinguish sarcoidosis from TB, physicians usually employ biopsy tissue diagnosis and blood tests; this approach is painful for patients,...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9144943/ https://www.ncbi.nlm.nih.gov/pubmed/35632254 http://dx.doi.org/10.3390/s22103846 |
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author | Baghdadi, Nadiah Maklad, Ahmed S. Malki, Amer Deif, Mohanad A. |
author_facet | Baghdadi, Nadiah Maklad, Ahmed S. Malki, Amer Deif, Mohanad A. |
author_sort | Baghdadi, Nadiah |
collection | PubMed |
description | Sarcoidosis is frequently misdiagnosed as tuberculosis (TB) and consequently mistreated due to inherent limitations in radiological presentations. Clinically, to distinguish sarcoidosis from TB, physicians usually employ biopsy tissue diagnosis and blood tests; this approach is painful for patients, time-consuming, expensive, and relies on techniques prone to human error. This study proposes a computer-aided diagnosis method to address these issues. This method examines seven EfficientNet designs that were fine-tuned and compared for their abilities to categorize X-ray images into three categories: normal, TB-infected, and sarcoidosis-infected. Furthermore, the effects of stain normalization on performance were investigated using Reinhard’s and Macenko’s conventional stain normalization procedures. This procedure aids in improving diagnostic efficiency and accuracy while cutting diagnostic costs. A database of 231 sarcoidosis-infected, 563 TB-infected, and 1010 normal chest X-ray images was created using public databases and information from several national hospitals. The EfficientNet-B4 model attained accuracy, sensitivity, and precision rates of 98.56%, 98.36%, and 98.67%, respectively, when the training X-ray images were normalized by the Reinhard stain approach, and 97.21%, 96.9%, and 97.11%, respectively, when normalized by Macenko’s approach. Results demonstrate that Reinhard stain normalization can improve the performance of EfficientNet -B4 X-ray image classification. The proposed framework for identifying pulmonary sarcoidosis may prove valuable in clinical use. |
format | Online Article Text |
id | pubmed-9144943 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-91449432022-05-29 Reliable Sarcoidosis Detection Using Chest X-rays with EfficientNets and Stain-Normalization Techniques Baghdadi, Nadiah Maklad, Ahmed S. Malki, Amer Deif, Mohanad A. Sensors (Basel) Article Sarcoidosis is frequently misdiagnosed as tuberculosis (TB) and consequently mistreated due to inherent limitations in radiological presentations. Clinically, to distinguish sarcoidosis from TB, physicians usually employ biopsy tissue diagnosis and blood tests; this approach is painful for patients, time-consuming, expensive, and relies on techniques prone to human error. This study proposes a computer-aided diagnosis method to address these issues. This method examines seven EfficientNet designs that were fine-tuned and compared for their abilities to categorize X-ray images into three categories: normal, TB-infected, and sarcoidosis-infected. Furthermore, the effects of stain normalization on performance were investigated using Reinhard’s and Macenko’s conventional stain normalization procedures. This procedure aids in improving diagnostic efficiency and accuracy while cutting diagnostic costs. A database of 231 sarcoidosis-infected, 563 TB-infected, and 1010 normal chest X-ray images was created using public databases and information from several national hospitals. The EfficientNet-B4 model attained accuracy, sensitivity, and precision rates of 98.56%, 98.36%, and 98.67%, respectively, when the training X-ray images were normalized by the Reinhard stain approach, and 97.21%, 96.9%, and 97.11%, respectively, when normalized by Macenko’s approach. Results demonstrate that Reinhard stain normalization can improve the performance of EfficientNet -B4 X-ray image classification. The proposed framework for identifying pulmonary sarcoidosis may prove valuable in clinical use. MDPI 2022-05-19 /pmc/articles/PMC9144943/ /pubmed/35632254 http://dx.doi.org/10.3390/s22103846 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Baghdadi, Nadiah Maklad, Ahmed S. Malki, Amer Deif, Mohanad A. Reliable Sarcoidosis Detection Using Chest X-rays with EfficientNets and Stain-Normalization Techniques |
title | Reliable Sarcoidosis Detection Using Chest X-rays with EfficientNets and Stain-Normalization Techniques |
title_full | Reliable Sarcoidosis Detection Using Chest X-rays with EfficientNets and Stain-Normalization Techniques |
title_fullStr | Reliable Sarcoidosis Detection Using Chest X-rays with EfficientNets and Stain-Normalization Techniques |
title_full_unstemmed | Reliable Sarcoidosis Detection Using Chest X-rays with EfficientNets and Stain-Normalization Techniques |
title_short | Reliable Sarcoidosis Detection Using Chest X-rays with EfficientNets and Stain-Normalization Techniques |
title_sort | reliable sarcoidosis detection using chest x-rays with efficientnets and stain-normalization techniques |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9144943/ https://www.ncbi.nlm.nih.gov/pubmed/35632254 http://dx.doi.org/10.3390/s22103846 |
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