Abstract
Pediatric pneumonia is a serious medical condition in which fluid fills the air sacs of the lungs. While chest X-rays have emerged as a more effective alternative to traditional diagnosis methods, the low radiation levels of X- rays in children have made accurate identification more challenging, leading to human-prone errors. To address this issue, deep learning architectures like Convolutional Neural Networks (CNNs) have been increasingly used for computer-aided diagnosis of chest X-ray images. In this paper, we propose an efficient Channel Attention (ECA) module attached to the end of pre-trained ResNet50 and DenseNet121, VGG19. We also present a weighted average ensemble based on our proposed model's performance. Our approach achieved an accuracy of 95.67%, precision of 94.81%, recall of 98.46%, F1 score of 96.60%, and an AUC curve of 94.74% on the pediatric pneumonia dataset. In conclusion, our proposed architecture holds promise for aiding in real-time pediatric pneumonia diagnosis and potentially improving patient outcomes.
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Asswin, C.R. et al. (2023). Weighted Average Ensemble Approach for Pediatric Pneumonia Diagnosis Using Channel Attention Deep CNN Architectures. In: Kadry, S., Prasath, R. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2023. Lecture Notes in Computer Science(), vol 13924. Springer, Cham. https://doi.org/10.1007/978-3-031-44084-7_24
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DOI: https://doi.org/10.1007/978-3-031-44084-7_24
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