COPD Assessment Through Multimodal Analysis: Exploiting the Synergy of CNNs and LSTM Networks

Chronic Obstructive Pulmonary Disease (COPD) is a prevalent respiratory condition that requires accurate assessment for effective management. The paper proposes a novel approach that leverages the combined power of CNNs and LSTM networks for COPD assessment through multimodal analysis. The objective of the study is to enhance the accuracy and reliability of COPD diagnosis by exploiting the synergy between CNNs and LSTM networks using a comprehensive dataset comprising lung function measurements, clinical history, and imaging data. Existing systems for COPD assessment often rely on single-modal analysis, limiting the effectiveness of the diagnosis. In contrast, our proposed approach integrates multiple modalities, including lung function measurements, clinical history, and imaging data, to capture a more comprehensive representation of the disease. Experimental evaluation on the dataset showcases the superior performance of our model, achieving an accuracy above 95 % and outperforming existing systems in terms of precision, recall, and Fl-score. The fusion of CNNs and LSTM networks enables the model to extract relevant features and capture temporal dependencies, enhancing the overall COPD assessment performance. These findings highlight the potential of multimodal analysis and the synergy between CNNs and LSTM networks for accurate and reliable early detection and diagnosis of COPD. The research contributes to improving the management and treatment outcomes of the debilitating respiratory condition.