REVOLUTIONIZING PNEUMONIA DIAGNOSIS: A DEEP LEARNING APPROACH TO CHEST X-RAY IMAGE ANALYSIS

Abstract

Pneumonia remains a major global health concern, emphasizing the need for accurate and efficient diagnostic tools(Asnaoui, et al, 2021). Deep learning models applied to chest X-ray images offer promising advancements in pneumonia detection, improving both speed and accuracy( Sarangi, et al, 2021). However, there is a lack of comprehensive evaluations of these models on diverse datasets, particularly in terms of their practical use in clinical settings. This study aims to fill this gap by analyzing pneumonia detection through chest X-ray images, bridging the divide between research outcomes and real-world application (Singh & Tripathi, 2022). The methodology includes data set prepossessing, implementation of a convolution neural network (CNN), and two per-trained models, MobileNetV2 and DenseNet121, with performance evaluated on training, validation, and test sets. Results demonstrate the models’ high accuracy and provide informative visualizations, highlighting their potential clinical significance in streamlining pneumonia diagnosis.(Rajpurkar et al,  (2017) This study offers valuable insights for healthcare professionals by presenting a reliable, rapid tool for pneumonia identification and contributing to the broader application of deep learning models in healthcare.