Inteligência artificial aplicada à radiografia de tórax: comparação de modelos de redes neurais convolucionais na detecção de opacidades.
Year: 2026
Type: article
Abstract: Introduction: Artificial intelligence, particularly through convolutional neural networks (CNNs), has shown increasing applicability in the automated analysis of medical images and may assist in identifying abnormalities on chest radiographs. Objective: To develop and compare the performance of different convolutional neural network architectures for classifying chest radiographs into Normal, Opacity, and Other Abnormalities categories, with emphasis on opacity detection. Methods: This retrospective, analytical, observational study analyzed 3,349 chest radiographs from patients aged 18 years or older. Three architectures were evaluated: EfficientNetB0 and MobileNetV2 using transfer learning, and a CNN developed and trained from scratch. Model performance was assessed using accuracy, precision, sensitivity (recall), F1-score, area under the receiver operating characteristic curve (AUC), and confusion matrices. Results: EfficientNetB0 achieved the best overall performance, with an accuracy of 85.9% and a macro AUC of approximately 0.96. Specifically for opacity detection, it achieved 84.6% precision, 82.2% sensitivity, and an F1-score of 83.4%. The CNN trained from scratch achieved an overall accuracy of 72.3%, with 83.5% precision, 57.6% sensitivity, and an F1-score of 68.2% for opacity detection, as well as evidence of overfitting. MobileNetV2 showed lower overall performance, with an accuracy of approximately 67%. Conclusion: EfficientNetB0 combined with transfer learning outperformed the other evaluated architectures, particularly in detecting opacities on chest radiographs. These findings demonstrate the potential of CNNs as auxiliary tools for radiographic analysis; however, external validation, greater image diversity, and improved model interpretability are required before clinical implementation.
Source: Revista fisio&terapia.
DOI: 10.69849/krv21j85
Authors: Maria Rita Bessa Muniz, Eugênia Maria das Chagas Silva, Rendrick Alexandre Alemão Rogério, Pedro Henrique Corrêa Teixeira, Ana Beatriz Rodrigues Calegario, Vinicius dos Santos Albuquerque, Carolina Augusta Modena Heming, Carolina Pontes Soares, Ronaldo Francisco Ribeiro Pereira, Rita de Cássia Ribeiro Pereira
Language: English
Topic: COVID-19 diagnosis using AI
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W7221046314