Predicting Pneumonia with Precision: A Deep Learning Approach

Abstract

Pneumonia is an infection that affects the lungs, causing inflammation in the air sacs, which then fill with fluid or pus. Common symptoms include coughing, difficulty breathing, fever, and chest pain. The condition can be triggered by bacteria, viruses, or fungi and can vary in severity from mild to life-threatening. Those at higher risk include infants, young children, older adults over 65, and individuals with weakened immune systems. Prompt diagnosis and treatment are crucial to prevent complications. This research explores how Machine Learning (ML) and Deep Learning (DL) can improve pneumonia detection. Using a dataset from Kaggle, we compare different models, including Random Forest and Deep Learning architectures like VGG-16, Inception V3, and various CNN structures. By evaluating models with different configurations, we aim to find the most reliable and accurate approach for detecting pneumonia effectively.