Leveraging Deep Learning for Enhanced Pneumonia Detection in Chest X-Rays

Early detection of pneumonia and acute respiratory infections caused by viruses and bacteria is critical for effective treatment and prevention of serious complications. Advanced technologies, especially AI and Deep learning, have proven valuable in enhancing this process. Our study introduces the Deep CNN (Convolutional Neural Networks) Algorithm, a modified deep convolutional neural network A model using chest X-rays to predict pneumonia in infected groups and uninfected groups by preprocessing. Using a dataset of 5,855 segmented images enhanced by data augmentation techniques, Deep CNN achieved a high classification accuracy of 91.46% for unseen images. This performance outperforms traditional models such as ImageNet, DenseNet, and VGG16, providing a model for accurate diagnosis of lung disease. Demonstrating high potential, the Deep CNN model represents a major advance in medical imaging technology, providing a reliable tool for early diagnosis and guiding treatment strategies. Its high accuracy highlights the potential of AI-driven processes to transform healthcare, improve patient outcomes, and support more effective and timely treatment.