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

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.