Early Detection of Lung Disease Using Deep Learning Algorithms on Image Data

Abstract

Abstract: This research paper presents a deep learning-based algorithm for the early detection of lung diseases using medical image data. The algorithm demonstrates high accuracy, sensitivity, specificity, precision, and AUC-ROC values, outperforming existing methods. By leveraging deep learning techniques, the algorithm provides a valuable tool for accurate disease identification, enabling timely interventions and improving patient outcomes. The study discusses the algorithm's performance, generalizability, and clinical relevance, highlighting its potential impact on clinical practice. Future work includes integrating multi-modal data, exploring model explainability, conducting external validation, and continuous model improvement to enhance the algorithm's diagnostic capabilities and real-world applicability. Overall, the proposed algorithm shows promise in advancing the early detection of lung diseases, contributing to improved healthcare outcomes