Addressing Noise Challenges in CNN-based Pneumonia Detection: A Study using Primary Indonesian Thoracic Imagery

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Accurate pneumonia diagnosis is vital, especially in resource-limited areas like Indonesia. While CNNs show promise for automated detection using chest X-rays, real-world image quality affects their performance. This study addresses this challenge by using a primary dataset—images directly from Indonesian patients—to avoid the biases of preprocessed secondary data. This ensures our findings are relevant to the Indonesian context. We tested how different noise types (salt-and-pepper and Gaussian) impact the accuracy of several common CNN architectures. These noise types mimic common image imperfections. Our analysis reveals that noise degrades the CNN's ability for 3% to 5% performance. This highlights the need for better pre-processing methods and potentially specialized CNN designs to handle noisy images. Ultimately, our work improves our understanding of deploying CNNs for pneumonia diagnosis in real-world settings, leading to more reliable and helpful diagnostic tools. Using primary data from diverse populations is crucial for building trustworthy AI in healthcare.

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