A Deep Learning Based Approach for Automated Diagnosis of Chronic Obstructive Pulmonary Disease using Chest X-Ray Images

Abstract

Chronic obstructive pulmonary disease is a dangerous and progressive lung disorder characterized by symptoms such as shortness of breath, coughing, and mucus production It is caused by long-term exposure to environmental contaminants such as cigarette smoking, chemical fumes, and dust. COPD detection is a key role in medical diagnosis, and a correct diagnosis of this condition is required for appropriate therapy. Deep learning techniques such as convolutional neural networks (CNNs) have shown promising results in image-processing tasks in recent years. In this study, The TensorFlow framework is used to diagnose COPD from lung X-ray pictures using a CNN-based technique. The proposed CNN architecture was trained on a large dataset of lung X-ray scans to understand the properties of normal and COPD patients Predictions from the model include performance metrics such as accuracy, precision, recall, and F1 score. The model's performance is validated using a validate large dataset, and the usability and functionality of the user interface are tested. The results demonstrate that deep learning models and user interfaces have the ability to diagnose COPD from medical pictures, potentially improving the efficiency and accuracy of COPD diagnosis and care The suggested approach has the potential to assist radiologists and clinicians in the early diagnosis of COPD and the timely commencement of appropriate management and therapy. Further research and development might have a substantial influence on improving patient outcomes and lowering healthcare expenses.