Developing a Novel Deep Learning Model for Pediatric Chest Radiograph Analysis
DOI:
https://doi.org/10.47611/jsrhs.v13i2.6759Keywords:
Convolutional Neural Network, Pediatric Chest X-ray, Object DetectionAbstract
Pediatric thoracic diseases pose significant challenges to healthcare systems worldwide, particularly in regions with limited access to specialized medical resources. Lower respiratory infections, in particular, contribute significantly to childhood morbidity and mortality. This research aims to address the diagnostic challenges associated with pediatric thoracic diseases by harnessing the power of artificial intelligence. The study utilizes the PediCXR dataset, composed of pediatric chest radiographs, and employs a Faster R-CNN model pretrained on ImageNet data for lesion detection and classification. The model is trained to identify abnormal lesions indicative of various thoracic diseases, with a focus on achieving high accuracy and minimizing loss. Experimental results demonstrate the model's capability to achieve an accuracy exceeding 90% and a loss under 0.1, meeting predefined objectives.
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Barakat, Natali, et al. "A Machine Learning Approach on Chest X-rays for Pediatric Pneumonia Detection." Digital Health, vol. 9, 4 June 2023, p. 20552076231180008. PubMed Central, https://doi.org/10.1177/20552076231180008. Accessed 7 Nov. 2023.
Chen, Kai-Chi, et al. "Diagnosis of Common Pulmonary Diseases in Children by X-ray Images and Deep Learning." Scientific Reports, vol. 10, 2020, https://doi.org/10.1038/s41598-020-73831-5. Accessed 7 Nov. 2023.
Chen, Yiyun, et al. "Deep Learning for Classification of Pediatric Chest Radiographs by WHO's Standardized Methodology." PLOS-ONE, 21 June 2021, https://doi.org/10.1371/journal.pone.0253239. Accessed 7 Nov. 2023.
E, Longjiang, et al. "Image-based Deep Learning in Diagnosing the Etiology of Pneumonia on Pediatric X-rays." Pediatric Pulmonology, vol. 56, no. 5, 19 Mar. 2021, pp. 1036-44. Wiley Online Library, https://doi.org/10.1002/ppul.25229. Accessed 7 Nov. 2023. Abstract.
Elgendi, Mohamed, et al. "The Effectiveness of Image Augmentation in Deep Learning Networks for Detecting COVID-19: A Geometric Transformation Perspective." Frontiers in Medicine, vol. 8, 1 Mar. 2021, https://doi.org/10.3389/fmed.2021.629134. Accessed 2 Jan. 2024.
"55 Countries Face a Health Worker Crunch Linked to COVID-19: WHO." United Nations, 14 Mar. 2023, news.un.org/en/story/2023/03/1134562. Accessed 1 Jan. 2024.
GBD 2015 LRI Collaborators. "Estimates of the Global, Regional, and National Morbidity, Mortality, and Aetiologies of Lower Respiratory Tract Infections in 195 Countries: A Systematic Analysis for the Global Burden of Disease Study 2015." The Lancet Infectious Diseases, vol. 17, no. 11, Nov. 2017, pp. 1133-61, https://doi.org/10.1016/S1473-3099(17)30396-1. Accessed 7 Nov. 2023.
Goldberger, A., et al. "PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220." (2000).
"Health Workforce." World Health Organization, www.who.int/health-topics/health-workforce#tab=tab_1. Accessed 1 Jan. 2024.
Leggiadro, Robert J. "Epidemics after Natural Disasters." The Pediatric Infectious Disease Journal, vol. 26, no. 6, June 2007, p. 552, https://doi.org/10.1097/INF.0b013e318054e34c. Accessed 1 Jan. 2024.
"Low Quality Healthcare Is Increasing the Burden of Illness and Health Costs Globally." World Health Organization, 5 July 2018, www.who.int/news/item/05-07-2018-low-quality-healthcare-is-increasing-the-burden-of-illness-and-health-costs-globally. Accessed 1 Jan. 2024.
Malik, Hassaan, et al. "CDC_Net: Multi-classification Convolutional Neural Network Model for Detection of COVID-19, Pneumothorax, Pneumonia, Lung Cancer, and Tuberculosis Using Chest X-rays." Multimedia Tools and Applications, vol. 82, 20 Sept. 2022, pp. 13855-80, https://doi.org/10.1007/s11042-022-13843-7. Accessed 3 Jan. 2024.
Nguyen, Ha Quy, et al. "VinDr-CXR: An open dataset of chest X-rays with radiologist annotations" (version 1.0.0). PhysioNet (2021), https://doi.org/10.13026/3akn-b287.
Nguyen, Ha Q., et al. "VinDr-CXR: An Open Dataset of Chest X-rays with Radiologist's Annotations." Scientific Data, vol. 9, 2022, https://doi.org/10.1038/s41597-022-01498-w. Accessed 7 Nov. 2023.
Padash, Sirwa, et al. "Pediatric Chest Radiograph Interpretation: How Far Has Artificial Intelligence Come? A Systematic Literature Review." Pediatric Radiology, vol. 52, 23 Apr. 2022, pp. 1568-80, https://doi.org/10.1007/s00247-022-05368-w. Accessed 2 Jan. 2024.
Pakenham-Walsh, Neil, and Frederick Bukachi. "Information Needs of Health Care Workers in Developing Countries: A Literature Review with a Focus on Africa." Human Resources for Health, vol. 7, no. 30, 8 Apr. 2009. PubMed Central, https://doi.org/10.1186/1478-4491-7-30. Accessed 1 Jan. 2024.
Pham, Hieu Huy, et al. "VinDr-PCXR: An open, large-scale pediatric chest X-ray dataset for interpretation of common thoracic diseases" (version 1.0.0). PhysioNet (2022), https://doi.org/10.13026/k8qc-na36.
Pham, Hieu H., et al. "PediCXR: An Open, Large-scale Chest Radiograph Dataset for Interpretation of Common Thoracic Diseases in Children." Scientific Data, vol. 10, 2023, https://doi.org/10.1038/s41597-023-02102-5. Accessed 7 Nov. 2023.
Rahman, Tawsifur, et al. "Transfer Learning with Deep Convolutional Neural Network (CNN) for Pneumonia Detection Using Chest X-ray." Applied Sciences, vol. 10, no. 9, 6 May 2020, p. 3233, https://doi.org/10.3390/app10093233. Accessed 1 Jan. 2024.
Ravi, Vinayakumar, et al. "A Cost-sensitive Deep Learning-based Meta-classifier for Pediatric Pneumonia Classification Using Chest X-rays." Expert Systems, vol. 39, no. 7, 23 Mar. 2022. Wiley Online Library, https://doi.org/10.1111/exsy.12966. Accessed 7 Nov. 2023. Abstract.
Tang, Yu-Xing, et al. "Automated Abnormality Classification of Chest Radiographs Using Deep Convolutional Neural Networks." npj Digital Medicine, vol. 3, 14 May 2020, https://doi.org/10.1038/s41746-020-0273-z. Accessed 1 Jan. 2024.
Türk, Fuat, and Yunus Kökver. "Detection of Lung Opacity and Treatment Planning with Three-Channel Fusion CNN Model." Arab Journal for Science and Engineering, 14 Apr. 2023, pp. 1-13. PubMed Central, https://doi.org/10.1007/s13369-023-07843-4. Accessed 13 Nov. 2023.
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