Research Article
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Year 2024, , 54 - 61, 26.03.2024
https://doi.org/10.46810/tdfd.1363218

Abstract

References

  • Hu Z, Yang Z, Lafata KJ, et al. A radiomics-boosted deep-learning model for COVID-19 and non-COVID-19 pneumonia classification using chest x-ray images. Med Phys. 2022; 49: 3213–3222.
  • Reshan MS Al, Gill KS, Anand V, et al. Detection of Pneumonia from Chest X-ray Images Utilizing MobileNet Model. Healthc; 11. Epub ahead of print 2023. DOI: 10.3390/healthcare11111561.
  • Jaiswal AK, Tiwari P, Kumar S, et al. Identifying pneumonia in chest X-rays: A deep learning approach. Meas J Int Meas Confed. 2019; 145: 511–518.
  • Singh S, Tripathi BK. Pneumonia classification using quaternion deep learning. Multimed Tools Appl. 2022; 81: 1743–1764.
  • Zhang D, Ren F, Li Y, et al. Pneumonia detection from chest x-ray images based on convolutional neural network. Electron; 10. Epub ahead of print 2021. DOI: 10.3390/electronics10131512.
  • Kundu R, Das R, Geem ZW, et al. Pneumonia detection in chest X-ray images using an ensemble of deep learning models. PLoS One; 16. Epub ahead of print 2021. DOI: 10.1371/journal.pone.0256630.
  • Mercaldo F, Belfiore MP, Reginelli A, et al. Coronavirus covid-19 detection by means of explainable deep learning. Sci Rep. 2023; 13: 1–11.
  • Ayan E, Ünver HM. Diagnosis of pneumonia from chest X-ray images using deep learning. 2019 Sci Meet Electr Biomed Eng Comput Sci EBBT 2019. İstanbul: IEEE; 2019; p. 0–4.
  • Sharma S, Guleria K. A Deep Learning based model for the Detection of Pneumonia from Chest X-Ray Images using VGG-16 and Neural Networks. Procedia Comput Sci. 2023; 218: 357–366.
  • Szepesi P, Szilágyi L. Detection of pneumonia using convolutional neural networks and deep learning. Biocybern Biomed Eng. 2022; 42: 1012–1022.
  • Al-Taani AT, Al-Dagamseh IT. Automatic Detection of Pneumonia Using Concatenated Convolutional Neural Network. Jordanian J Comput Inf Technol. 2023; 9: 118–136.
  • Shah U, Abd-Alrazeq A, Alam T, et al. An efficient method to predict pneumonia from chest X-rays using deep learning approach. Stud Health Technol Inform 2020; 272: 457–460.
  • Stephen O, Sain M, Maduh UJ, et al. An Efficient Deep Learning Approach to Pneumonia Classification in Healthcare. J Healthc Eng; 2019. Epub ahead of print 2019. DOI: 10.1155/2019/4180949.
  • Rajaraman S, Candemir S, Kim I, et al. Visualization and interpretation of convolutional neural network predictions in detecting pneumonia in pediatric chest radiographs. Appl Sci; 8. Epub ahead of print 2018. DOI: 10.3390/app8101715.
  • Toğaçar M, Ergen B, Cömert Z, et al. A Deep Feature Learning Model for Pneumonia Detection Applying a Combination of mRMR Feature Selection and Machine Learning Models. Irbm. 2020; 41: 212–222.
  • Chouhan V, Singh SK, Khamparia A, et al. A novel transfer learning based approach for pneumonia detection in chest X-ray images. Appl Sci; 10. Epub ahead of print 2020. DOI: 10.3390/app10020559.
  • Mooney P. Chest X-Ray Images (Pneumonia). Kaggle, https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia (accessed 10 September 2023).
  • Sandler M, Howard A, Zhu M, et al. MobileNetV2: Inverted Residuals and Linear Bottlenecks. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit 2018. Salt Lake City: IEEE; 2018. p. 4510–4520.
  • Howard AG, Zhu M, Chen B, et al. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications, http://arxiv.org/abs/1704.04861 (2017).
  • Hu J, Shen L, Sun G. Squeeze-and-Excitation Networks. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit 2018. Salt Lake City: IEEE; 2018. p.7132–7141.
  • Fırat H. Sıkma - Uyarma Artık Ağı kullanılarak Beyaz Kan Hücrelerinin Sınıflandırılması. Bilişim Teknol Derg. 2023; 16: 189–205.
  • Asker ME. Hyperspectral image classification method based on squeeze-and-excitation networks, depthwise separable convolution and multibranch feature fusion. Earth Sci Informatics. 2023; 1427–1448.
  • Dayı B, Üzen H, Çiçek İB, et al. A Novel Deep Learning-Based Approach for Segmentation of Different Type Caries Lesions on Panoramic Radiographs. Diagnostics. 2023; 13: 202.

Detection of Pneumonia Using A Hybrid Approach Consisting of MobileNetV2 and Squeeze-and-Excitation Network

Year 2024, , 54 - 61, 26.03.2024
https://doi.org/10.46810/tdfd.1363218

Abstract

Pneumonia is a global health concern, responsible for a significant number of deaths. Its diagnostic challenge arises from visual similarities it shares with various respiratory diseases, such as tuberculosis, complicating accurate identification. Furthermore, the variability in acquiring and processing chest X-ray (CXR) images can impact image quality, posing a hurdle for dependable algorithm development. To address this, resilient data-centric algorithms, trained on comprehensive datasets and validated through diverse imaging methods and radiology expertise, are imperative. This study presents a deep learning approach designed to distinguish between normal and pneumonia cases. The model, a hybrid of MobileNetV2 and the Squeeze-and-Excitation (SE) block, aims to reduce learnable parameters while enhancing feature extraction and classification. Integration of the SE block enhances classification performance, despite a slight parameter increase. The model was trained and tested on a dataset of 5856 CXR images from Kaggle's medical imaging challenge. Results demonstrated the model's exceptional performance, achieving an accuracy of 98.81%, precision of 98.79%, recall rate of 98.24%, and F1-score of 98.51%. Comparative analysis with various Convolutional neural network-based pre-trained models and recent literature studies confirmed its superiority, solidifying its potential as a robust tool for pneumonia detection, thus addressing a critical healthcare need.

References

  • Hu Z, Yang Z, Lafata KJ, et al. A radiomics-boosted deep-learning model for COVID-19 and non-COVID-19 pneumonia classification using chest x-ray images. Med Phys. 2022; 49: 3213–3222.
  • Reshan MS Al, Gill KS, Anand V, et al. Detection of Pneumonia from Chest X-ray Images Utilizing MobileNet Model. Healthc; 11. Epub ahead of print 2023. DOI: 10.3390/healthcare11111561.
  • Jaiswal AK, Tiwari P, Kumar S, et al. Identifying pneumonia in chest X-rays: A deep learning approach. Meas J Int Meas Confed. 2019; 145: 511–518.
  • Singh S, Tripathi BK. Pneumonia classification using quaternion deep learning. Multimed Tools Appl. 2022; 81: 1743–1764.
  • Zhang D, Ren F, Li Y, et al. Pneumonia detection from chest x-ray images based on convolutional neural network. Electron; 10. Epub ahead of print 2021. DOI: 10.3390/electronics10131512.
  • Kundu R, Das R, Geem ZW, et al. Pneumonia detection in chest X-ray images using an ensemble of deep learning models. PLoS One; 16. Epub ahead of print 2021. DOI: 10.1371/journal.pone.0256630.
  • Mercaldo F, Belfiore MP, Reginelli A, et al. Coronavirus covid-19 detection by means of explainable deep learning. Sci Rep. 2023; 13: 1–11.
  • Ayan E, Ünver HM. Diagnosis of pneumonia from chest X-ray images using deep learning. 2019 Sci Meet Electr Biomed Eng Comput Sci EBBT 2019. İstanbul: IEEE; 2019; p. 0–4.
  • Sharma S, Guleria K. A Deep Learning based model for the Detection of Pneumonia from Chest X-Ray Images using VGG-16 and Neural Networks. Procedia Comput Sci. 2023; 218: 357–366.
  • Szepesi P, Szilágyi L. Detection of pneumonia using convolutional neural networks and deep learning. Biocybern Biomed Eng. 2022; 42: 1012–1022.
  • Al-Taani AT, Al-Dagamseh IT. Automatic Detection of Pneumonia Using Concatenated Convolutional Neural Network. Jordanian J Comput Inf Technol. 2023; 9: 118–136.
  • Shah U, Abd-Alrazeq A, Alam T, et al. An efficient method to predict pneumonia from chest X-rays using deep learning approach. Stud Health Technol Inform 2020; 272: 457–460.
  • Stephen O, Sain M, Maduh UJ, et al. An Efficient Deep Learning Approach to Pneumonia Classification in Healthcare. J Healthc Eng; 2019. Epub ahead of print 2019. DOI: 10.1155/2019/4180949.
  • Rajaraman S, Candemir S, Kim I, et al. Visualization and interpretation of convolutional neural network predictions in detecting pneumonia in pediatric chest radiographs. Appl Sci; 8. Epub ahead of print 2018. DOI: 10.3390/app8101715.
  • Toğaçar M, Ergen B, Cömert Z, et al. A Deep Feature Learning Model for Pneumonia Detection Applying a Combination of mRMR Feature Selection and Machine Learning Models. Irbm. 2020; 41: 212–222.
  • Chouhan V, Singh SK, Khamparia A, et al. A novel transfer learning based approach for pneumonia detection in chest X-ray images. Appl Sci; 10. Epub ahead of print 2020. DOI: 10.3390/app10020559.
  • Mooney P. Chest X-Ray Images (Pneumonia). Kaggle, https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia (accessed 10 September 2023).
  • Sandler M, Howard A, Zhu M, et al. MobileNetV2: Inverted Residuals and Linear Bottlenecks. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit 2018. Salt Lake City: IEEE; 2018. p. 4510–4520.
  • Howard AG, Zhu M, Chen B, et al. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications, http://arxiv.org/abs/1704.04861 (2017).
  • Hu J, Shen L, Sun G. Squeeze-and-Excitation Networks. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit 2018. Salt Lake City: IEEE; 2018. p.7132–7141.
  • Fırat H. Sıkma - Uyarma Artık Ağı kullanılarak Beyaz Kan Hücrelerinin Sınıflandırılması. Bilişim Teknol Derg. 2023; 16: 189–205.
  • Asker ME. Hyperspectral image classification method based on squeeze-and-excitation networks, depthwise separable convolution and multibranch feature fusion. Earth Sci Informatics. 2023; 1427–1448.
  • Dayı B, Üzen H, Çiçek İB, et al. A Novel Deep Learning-Based Approach for Segmentation of Different Type Caries Lesions on Panoramic Radiographs. Diagnostics. 2023; 13: 202.
There are 23 citations in total.

Details

Primary Language English
Subjects Information Systems (Other)
Journal Section Articles
Authors

Hüseyin Fırat 0000-0002-1257-8518

Hüseyin Üzen 0000-0002-0998-2130

Early Pub Date March 26, 2024
Publication Date March 26, 2024
Published in Issue Year 2024

Cite

APA Fırat, H., & Üzen, H. (2024). Detection of Pneumonia Using A Hybrid Approach Consisting of MobileNetV2 and Squeeze-and-Excitation Network. Türk Doğa Ve Fen Dergisi, 13(1), 54-61. https://doi.org/10.46810/tdfd.1363218
AMA Fırat H, Üzen H. Detection of Pneumonia Using A Hybrid Approach Consisting of MobileNetV2 and Squeeze-and-Excitation Network. TDFD. March 2024;13(1):54-61. doi:10.46810/tdfd.1363218
Chicago Fırat, Hüseyin, and Hüseyin Üzen. “Detection of Pneumonia Using A Hybrid Approach Consisting of MobileNetV2 and Squeeze-and-Excitation Network”. Türk Doğa Ve Fen Dergisi 13, no. 1 (March 2024): 54-61. https://doi.org/10.46810/tdfd.1363218.
EndNote Fırat H, Üzen H (March 1, 2024) Detection of Pneumonia Using A Hybrid Approach Consisting of MobileNetV2 and Squeeze-and-Excitation Network. Türk Doğa ve Fen Dergisi 13 1 54–61.
IEEE H. Fırat and H. Üzen, “Detection of Pneumonia Using A Hybrid Approach Consisting of MobileNetV2 and Squeeze-and-Excitation Network”, TDFD, vol. 13, no. 1, pp. 54–61, 2024, doi: 10.46810/tdfd.1363218.
ISNAD Fırat, Hüseyin - Üzen, Hüseyin. “Detection of Pneumonia Using A Hybrid Approach Consisting of MobileNetV2 and Squeeze-and-Excitation Network”. Türk Doğa ve Fen Dergisi 13/1 (March 2024), 54-61. https://doi.org/10.46810/tdfd.1363218.
JAMA Fırat H, Üzen H. Detection of Pneumonia Using A Hybrid Approach Consisting of MobileNetV2 and Squeeze-and-Excitation Network. TDFD. 2024;13:54–61.
MLA Fırat, Hüseyin and Hüseyin Üzen. “Detection of Pneumonia Using A Hybrid Approach Consisting of MobileNetV2 and Squeeze-and-Excitation Network”. Türk Doğa Ve Fen Dergisi, vol. 13, no. 1, 2024, pp. 54-61, doi:10.46810/tdfd.1363218.
Vancouver Fırat H, Üzen H. Detection of Pneumonia Using A Hybrid Approach Consisting of MobileNetV2 and Squeeze-and-Excitation Network. TDFD. 2024;13(1):54-61.