Air Pollution Attribute-Based Lung Disease Detection with the RESNET Deep Learning Algorithm

Air pollution causes respiratory illnesses. Pollution harms respiratory health, thus accurate disease detection is essential. Deep learning is used to diagnose lung issues by examining pollutant attributes. We compared RESNET deep learning sickness categorization with CNN and RNN. Pollution and lung disease labels were collected for our study. PM, NO2, O3, and CO indicate air pollution. We verify data compatibility with deep learning models. Lung diseases are classified using RESNET, CNN, and RNN. Train algorithms utilizing pollutant characteristics and sickness diagnosis data. Loss functions and performance criteria improve models during training. We evaluate deep learning methods using accuracy, precision, recall, and F1-score. The algorithms' environmental parameter lung disease diagnosis accuracy is shown by the measurements. CNN and RNN are less accurate in diagnosing illness than RESNET. RESNET detects pollution-related lung diseases with higher precision, recall, and F1-score. This study shows that deep learning systems like RESNET can reliably diagnose lung illnesses using pollutant attributes. The findings improve environmental well-being by providing a reliable and efficient method for disease detection and risk assessment in contaminated environments.