Review of Early Detection of Lung Diseases based on Deep Learning Models

Earlier diagnosis of pulmonary disease is greatly significant in enhancing treatment impacts and lowering systems' medical workload. As increasingly more cases of pulmonary disease accumulate, methods of deep learning (DL) have increasingly become a viable option towards assisting physicians with diagnoses, particularly through the interpretation of chest X-ray (CXR) images. This paper presents the latest DL-based models and methods for early detection of lung diseases and evaluates their performance and accuracy of disease classification. It also demonstrates the power of ensemble learning methods, a combination of ResNet, EfficientNet, and Inception models, for enhancing the accuracy and reliability of diagnosis systems, especially in handling complicated patterns of diseases. The study seeks to introduce newer directions for research and explore the direction towards intelligent and scalable diagnosis solutions that can potentially make a critical contribution towards enhanced early detection and improving the quality of patient care.