AI Article Synopsis
- Pneumonia is a widespread infectious disease, and chest X-ray (CXR) is the primary method for diagnosis, but interpreting these images can be challenging due to the similarity between various conditions.
- A new convolutional neural network (CNN) model is proposed to enhance pneumonia detection by erasing pneumonia areas in CXR images and treating them as non-pneumonia samples, helping the model focus specifically on the disease.
- The results indicate that this improved CNN model outperforms existing leading detection methods in both accuracy and minimizing false positives.
Article Abstract
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.
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| http://dx.doi.org/10.1109/EMBC.2019.8857277 | DOI Listing |
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