A Comprehensive Review of Machine Learning Techniques for Pneumonia Detection from Chest X-ray Images

Abstract

Pneumonia, a significant contributor to global respiratory illnesses, demands prompt diagnosis for successful treatment. Chest X-rays, being widely accessible and cost-effective, serve as the primary imaging modality for pneumonia detection. However, manual interpretation of chest X-rays is subjective and susceptible to errors due to the often subtle and diverse visual indicators of pneumonia. This study delves into the potential of deep learning, a subfield of artificial intelligence, to automate pneumonia detection utilizing chest X-ray images. By leveraging the well-established VGG-16 model, a convolutional neural network architecture renowned for its image recognition capabilities, the study aims to develop a robust and accurate classification system. This system, if successful, has the potential to alleviate the burden on healthcare professionals by assisting in the timely and efficient detection of pneumonia, ultimately contributing to improved patient outcomes. Key Words: Deep Learning, VGG16, CNN, Pneumonia, Chest X-rays .