Leveraging deep learning for precise chronic bronchitis identification in X-ray modalities

Image processing plays a vital role in various fields such as autonomous systems, healthcare, and cataloging, especially when integrated with deep learning (DL). It is crucial in medical diagnostics, including the early detection of diseases like chronic obstructive pulmonary disease (COPD), which claimed 3.2 million lives in 2015. COPD, a life-threatening condition often caused by prolonged exposure to lung irritants and smoking, progresses through stages. Early diagnosis through image processing can significantly improve survival rates. COPD encompasses chronic bronchitis (CB) and emphysema; CB particularly increases in smokers and generally affects individuals between 50 and 70 years old. It damages the lungs’ air sacs, reducing oxygen transport and causing symptoms like coughing and shortness of breath. Treatments such as beta-agonists and inhaled steroids are used to manage symptoms and prolong lung function. Moreover, COVID-19 poses an additional risk to individuals with CB due to its impact on the respiratory system. The proposed system utilizes convolutional neural networks (CNN) to diagnose CB. In this system, CNN extracts essential and significant features from X-ray modalities, which are then fed into the neural network. The network undergoes training to recognize patterns and make accurate predictions based on the learned features. By leveraging DL techniques, the system aims to enhance the precision and reliability of CB detection. Our research specifically focuses on a subset of 189 lung disease images, carefully selected for model evaluation. To further refine the training process, various data augmentation and noise removal techniques are implemented. These techniques significantly enhance the quality of the training data, improving the model’s robustness and generalizability. As a result, the diagnostic accuracy has improved from 98.6% to 99.2%. This advancement not only validates the efficacy of our proposed model but also represents a significant improvement over existing literature. It highlights the potential of CNN-based approaches in transforming medical diagnostics through refined image analysis, learning capabilities, and automated feature extraction.