Machine Learning Based Pneumonia Detection Using X-ray Images

Artificial Intelligence And Machine Learning

Pneumonia using chest Xray images (CXR)has undergone a paradigm shift with the integration of artificial intelligence and Machine Learning techniques. This paper presents a thorough review of the developments in AI driven pneumonia detection, emphasizing the methodologies, challenges, and future prospects. Traditional diagnostic approaches relying on clinical symptoms and radiological findings are often subjective and time- consuming. The emergence of AI-powered system offers a promising solution by enabling automated and objective analysis of CXR images, there by aiding in early and accurate diagnosis. Various AI and ML methods, comprising convolutional neural networks (CNNs) and ensemble methods, have been leveraged to enhance diagnosis accuracy by removing discriminative characteristics from CXR images, leading to improved diagnostic accuracy. However, challenges such as data heterogeneity, interpretability of AI models, and ethical. considerations persist. Future research directions aim to address these challenges through multimodal data fusion, standardization of evaluation protocols, and regulatory guidelines. Overall, AI-driven pneumonia detection represents a transformative approach in respiratory medicine, with the potential to enhance diagnostic capabilities and improve patient outcome on global scale.

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