Auxílio ao Diagnóstico de Pneumonia por Análise de Imagens de Raio-X Utilizando Aprendizado de Máquina
Resumo
A pneumonia é uma doença pulmonar que exige diagnóstico rápido. Este trabalho propõe o uso de Inteligência Artificial (IA) para auxiliar nesse diagnóstico, analisando imagens de raio-x para identificar a presença e o tipo de pneumonia (viral ou bacteriana) antes do laudo do especialista. Foram testados modelos de Regressão Linear (RL) e Redes Neurais Artificiais (RNA). As Redes Neurais apresentaram os melhores resultados, com acurácia de 96,2% na detecção da doença e 91,3% na identificação do seu tipo. O estudo conclui que a ferramenta tem grande potencial para aplicação hospitalar, agilizando o diagnóstico e o tratamento.Referências
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Mujahid, M., Rustam, F., Chakrabarti, P., Mallampati, B., de la Torre Diez, I., Gali, P., ... & Ashraf, I. (2024). Pneumonia detection on chest X-rays from Xception-based transfer learning and logistic regression. Technology and Health Care, 32(6), 3847-3870.
Naseri, H., & Safaei, A. A. (2025). Diagnosis and prognosis of melanoma from dermos-copy images using machine learning and deep learning: a systematic literature review. BMC cancer, 25(1), 75.
Nazish, Ullah, S. I., Salam, A., Ullah, W., & Imad, M. (2021, March). COVID-19 lung image classification based on logistic regression and support vector machine. In European, Asian, Middle Eastern, North African Conference on Management & Information Systems (pp. 13-23). Cham: Springer International Publishing.
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Rajaraman, S., Liang, Z., Marini, N., Xue, Z., & Antani, S. (2025, June). The Hidden Threat of Hallucinations in Binary Chest X-Ray Pneumonia Classification. In 2025 IEEE 38th International Symposium on Computer-Based Medical Systems (CBMS) (pp. 668-673). IEEE.
Tiwari, V., Singhal, A., & Dhankhar, N. (2024). COVID-19 Detection from Chest X-Ray Using a Customized Artificial Neural Network. In Applied Intelligence for Medical Image Analysis (pp. 201-212). Apple Academic Press.
Zhang, J., Tian, B., Tian, M., Si, X., Li, J., & Fan, T. (2025). A scoping review of advancements in machine learning for glaucoma: current trends and future direction. Frontiers in Medicine, 12, 1573329.
Degadwala, S., Vyas, D., Biswas, H., Chakraborty, U., & Saha, S. (2021, July). Image captioning using inception V3 transfer learning model. In 2021 6th international conference on communication and electronics systems (ICCES) (pp. 1103-1108). IEEE.
Mujahid, M., Rustam, F., Chakrabarti, P., Mallampati, B., de la Torre Diez, I., Gali, P., ... & Ashraf, I. (2024). Pneumonia detection on chest X-rays from Xception-based transfer learning and logistic regression. Technology and Health Care, 32(6), 3847-3870.
Naseri, H., & Safaei, A. A. (2025). Diagnosis and prognosis of melanoma from dermos-copy images using machine learning and deep learning: a systematic literature review. BMC cancer, 25(1), 75.
Nazish, Ullah, S. I., Salam, A., Ullah, W., & Imad, M. (2021, March). COVID-19 lung image classification based on logistic regression and support vector machine. In European, Asian, Middle Eastern, North African Conference on Management & Information Systems (pp. 13-23). Cham: Springer International Publishing.
Paul, M. (2019). Chest x-ray images (pneumonia).
Rajaraman, S., Liang, Z., Marini, N., Xue, Z., & Antani, S. (2025, June). The Hidden Threat of Hallucinations in Binary Chest X-Ray Pneumonia Classification. In 2025 IEEE 38th International Symposium on Computer-Based Medical Systems (CBMS) (pp. 668-673). IEEE.
Tiwari, V., Singhal, A., & Dhankhar, N. (2024). COVID-19 Detection from Chest X-Ray Using a Customized Artificial Neural Network. In Applied Intelligence for Medical Image Analysis (pp. 201-212). Apple Academic Press.
Zhang, J., Tian, B., Tian, M., Si, X., Li, J., & Fan, T. (2025). A scoping review of advancements in machine learning for glaucoma: current trends and future direction. Frontiers in Medicine, 12, 1573329.
Publicado
12/11/2025
Como Citar
BUTTENBENDER, Darlene; MELO, Márcio R.; FIGUEIREDO, Rodrigo M.; MALLMANN, Ana P..
Auxílio ao Diagnóstico de Pneumonia por Análise de Imagens de Raio-X Utilizando Aprendizado de Máquina. In: ESCOLA REGIONAL DE APRENDIZADO DE MÁQUINA E INTELIGÊNCIA ARTIFICIAL DA REGIÃO SUL (ERAMIA-RS), 1. , 2025, Porto Alegre/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 276-279.
DOI: https://doi.org/10.5753/eramiars.2025.16641.
