An Analysis of Deep Learning Algorithms for Detection of Pulmonary Illness

The necessity for highly precise and rapid methods of diagnosis is highlighted by the important role that pulmonary illnesses make to the global health burden. Enhancing diagnostic accuracy and speeding up detection through the use of algorithms that use deep learning presents a substantial opportunity for better patient outcomes and more efficient healthcare procedures. In proposed article, the authors provide the special technique for recognizing pulmonary illnesses from medical imaging data by using deep learning algorithms. We offer a thorough method that integrates CNNs with additional deep learning methods to diagnose diseases with high accuracy. Through a detailed literature review, we highlight the existing research in this domain and identify gaps and challenges. We discuss the methodology, techniques employed, challenges encountered, and potential future directions. The author’s findings show how well the suggested method works to identify pulmonary diseases, finding the exact path for enhanced diagnostic systems in healthcare. Our study demonstrates algorithms in pulmonary disease detection, outperforming traditional methods, ensuring robustness across diverse datasets, and offering clinical utility with interpretability insights.