A review on key algorithms for pneumonia detection in X-ray images.

Embarking on a thorough exploration of pneumonia classification through X-ray images, this paper delves into the application of advanced machine learning methodologies, including Convolutional Neural Networks (CNN), Support Vector Machines (SVM), and Random Forest. With a focus on augmenting diagnostic accuracy and expediting decision-making, the integration of medical imaging with machine learning has become integral to pneumonia diagnosis. The review critically evaluates and compares the strengths and limitations of CNN, SVM, and Random Forest algorithms, aiming to contribute valuable insights into their applications. Without specifying a particular source, the paper delves into the operational dynamics of machine learning techniques in pneumonia classification, seeking to stimulate discussions and advance the understanding of their role. The overarching objective is to enrich the discourse and foster progress in medical imaging, ultimately enhancing patient outcomes through more precise and efficient pneumonia diagnosis. [ABSTRACT FROM AUTHOR]