Pneumonia Detection Using Deep Learning: A CNN-Based Approach

Pneumonia is a deadly lung infection which can lead to life-threatening complications if left undiagnosed and untreated.Traditional diagnosis depends on radiologists reading chest X-rays manually, a time-consuming process prone to human error.Mistakes in diagnosis causes delayed or improper treatment and severe health impacts or even fatality.Following the growth of deep learning methods, automatic medical image analysis is becoming an increasingly potential means to enhance the accuracy and efficiency of diagnoses.To tackle these challenges, we propose a deep learning-based model for automated pneumonia detection using Convolutional Neural Networks (CNNs).Our research leverages the publicly available chest X-ray dataset from Kaggle to train a custom CNN model that includes three convolutional layers, batch normalization, dropout regularization, and an Adam optimizer.The model achieved an impressive test accuracy of 85.74%, showcasing its potential to aid in clinical decision-making.Additionally, this study looks into how data augmentation affects performance and considers ways to improve the model's generalization and robustness.