Lung Disease Recognition Using Ensemble Deep Net Model for Disease Identification and Classification

This research introduces a groundbreaking approach for identification of lung diseases, with a focus on improving patient outcomes and easing the burden on healthcare systems.The primary goal is to enable precise prediction of lung diseases at an early stage.The proposed hybrid model, CNN-GRU-LSTM, is employed for analyzing sequential lung images.An Ensemble Deep Net model is introduced, incorporating CNNs, VGG-based neural networks, and Caps networks.A key innovation is the Spatio Temporal Deep (STD) feature, which captures global and local contextual information across multiple frames.The evaluation of the hybrid model on a diverse dataset demonstrates its effectiveness in identifying subtle patterns indicative of early-stage lung diseases.The Ensemble Deep Net model achieves exceptional accuracy in lesion detection within the complex and variable landscape of lung nodules.The Ensemble Deep Net is leveraged to achieve a remarkable 100% accuracy in classifying diverse lung diseases.This research marks a significant advancement in pulmonary healthcare by providing a valuable tool for early diagnosis and intervention in lung diseases.Also, it holds great promise for enhancing preventive strategies in pulmonary medicine offering a revolutionary path for improving patient care.