9th International Conference on ICT for Sustainable Development

Authors - Vivekanand Thakare, Shailendra S. Aote
Abstract - Prognostic predictions play a crucial role in guiding clinical decision-making and optimizing patient care pathways in lung diseases. The innovative perspective has been proposed here using a Hybrid Convolutional Neural Network (CNN) architecture with an integrated Attention Mechanism for prognostic predictions in respiratory diseases. Leveraging the rich spatial information encoded in chest computed tomography (CT) images, our model aims to accurately predict disease progression, severity, and treatment outcomes across a spectrum of lung diseases, including lung cancer, COVID-19, chronic obstructive pulmonary disease (COPD), tuberculosis (TB), and pneumonia. The Hybrid CNN architecture combines convolutional layers for feature extraction with attention mechanisms for focusing on informative regions and features within the CT images. The evaluation has been performed on our model with the available dataset using CT scan as well as X-ray images diagnosed with various lung diseases. The results demonstrate the efficacy of our approach in achieving high accuracy and precision in prognostic predictions, with promising clinical implications for finalizing treatment tactics in order to improve patient outcomes. Our study especially focuses on Hybrid CNN with Attention Mechanism of deep learning techniques, in advancing prognostic assessment and clinical decision-making in pulmonary medicine.