X-ray Classification and Prediction of Lung Disease Using Convolutional Neural Network (CNN)

Lung disease diagnosis is crucial for timely detection and effective treatment. Deep learning techniques, such as Convolutional Neural Networks (CNNs), have shown remarkable success in image classification tasks. This study aims to develop a lung disease prediction model using CNNs applied to X-ray images. The model utilizes a labelled dataset of lung X-ray images containing both healthy cases and various lung abnormalities. Pre-processing techniques are applied to enhance image quality and extract relevant features. CNN architecture is employed to learn and classify the lung images, leveraging its ability to capture spatial features. The dataset is divided into training and validation sets to optimize model parameters by minimizing prediction errors during training. Hyper parameter tuning is performed to identify the optimal combination of learning rate, batch size, and network architecture. Evaluation of the trained model is conducted on the validation set using standard metrics like accuracy, precision, recall, and F1-score. The model's performance is assessed across different lung disease classes to identify biases or weaknesses. To assess generalizati on and real-world performance, a separate testing set is used, distinct from the training and validation sets. Continuous monitoring and updates are essential to ensure the model remains accurate and up-to-date, especially with the availability of new data or the need for improvements. The proposed lung disease prediction model holds promise in assisting healthcare professionals with early detection and diagnosis of lung abnormalities. It has the potential to enhance patient outcomes and alleviate the burden on healthcare systems.