Published Paper Details:

Pneumonia is a serious respiratory disease that affects millions of people worldwide and can be life-threatening if not diagnosed and treated early. Traditional diagnosis of pneumonia is primarily based on clinical examination and interpretation of chest X-ray images by radiologists. However, this manual process can be time-consuming, subjective, and prone to human error, especially in regions with limited access to expert healthcare professionals. To address these challenges, the application of Deep Learning, particularly Convolutional Neural Networks (CNNs), has emerged as an efficient and reliable approach for automated pneumonia detection. This project focuses on developing a computer-aided diagnostic system that utilizes CNN models to classify chest X-ray images as pneumonia-infected or normal. CNNs are a class of deep neural networks specifically designed for image processing tasks, capable of automatically extracting relevant features such as edges, textures, and patterns from medical images. In this study, a large dataset of labeled chest X-ray images is used to train the model, enabling it to learn distinguishing characteristics of pneumonia. The proposed system involves several stages, including image preprocessing, data augmentation, model training, validation, and testing. Preprocessing techniques such as resizing, normalization, and noise reduction are applied to improve image quality and enhance model performance. Data augmentation methods like rotation, flipping, and zooming are used to increase dataset diversity and prevent overfitting. The CNN architecture typically consists of multiple convolutional layers, pooling layers, and fully connected layers that work together to extract features and perform classification. The trained model is evaluated using performance metrics such as accuracy, precision, recall, and F1-score to ensure reliability and effectiveness. Experimental results demonstrate that CNN-based models can achieve high accuracy in detecting pneumonia from chest X-ray images, often outperforming traditional machine learning methods. This automated system can assist radiologists in making faster and more accurate diagnoses, thereby improving patient outcomes..