Multi-class Lung Diseases Prediction using Deep Learning Algorithm

Abstract

 Lung diseases remain one of the leading causes of mortality worldwide, emphasizing the need for accurate and timely diagnosis. Traditional diagnostic methods, while effective, are often time-consuming and prone to human error. This research presents a deep learning-based approach for multi-class lung disease prediction using chest X-ray and CT scan images. The system utilizes Convolutional Neural Networks (CNNs) and transfer learning techniques with pre-trained models such as DenseNet to classify various lung conditions including Pneumonia, Tuberculosis, COVID-19, Lung Cancer, and Normal lungs. The model is trained on publicly available datasets like ChestX-ray14, CheXpert, and the COVID-19 Radiography Database, incorporating preprocessing techniques such as image resizing, normalization, and data augmentation to improve generalization. A Softmax classifier is used at the output layer for multi-class classification. Performance metrics including accuracy, precision, recall, F1-score, and confusion matrix are used to evaluate the system’s effectiveness. Experimental results demonstrate high classification accuracy and robustness across multiple lung disease categories. The proposed deep learning model can assist radiologists in faster and more reliable diagnosis, potentially reducing the diagnostic burden and improving patient outcomes. This system highlights the potential of artificial intelligence in revolutionizing medical imaging and healthcare delivery.