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Annu Int Conf IEEE Eng Med Biol Soc ; 2019: 4851-4854, 2019 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-31946947

RESUMO

Pneumonia is a common infectious disease in the world. Its main diagnostic method is chest X-ray (CXR) examination. However, the high visual similarity between a large number of pathologies in CXR makes the interpretation and differentiation of pneumonia a challenge. In this paper, we propose an improved convolutional neural network (CNN) model for pneumonia detection. In order to guide the CNN to focus on disease-specific attended region, the pneumonia area of image is erased and marked as a non-pneumonia sample. In addition, transfer learning is used to segment the interest region of lungs to suppress background interference. The experimental results show that the proposed method is superior to the state-of-the-art object detection model in terms of accuracy and false positive rate.


Assuntos
Redes Neurais de Computação , Pneumonia , Interpretação de Imagem Radiográfica Assistida por Computador , Atenção , Humanos , Pneumonia/diagnóstico por imagem , Radiografia Torácica
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