Advancing COPD Diagnosis: A Deep Transfer Learning Approach Using ResNet-50 and ANN for Enhanced CT Scan Analysis

The COPD detection introduces an approach innovated by deep transfer learning on ECG signals. There is integration of Artificial Neural Network architecture and that comes along with ResNet-50 in this system for efficient use in extracting the useful feature of ECG signal about accurate COPD detection. This all-inclusive use of ECG signals provides an overall evaluation of both cardiac and respiratory functions, allowing COPD to be diagnosed promptly through the evaluation of subtle ECG pattern changes. The usability and cost-effectiveness are improved by the ubiquitous existence of ECG, enabling it to be implemented anywhere in the health system and even in less developed sites. Deep transfer learning techniques improve diagnostic accuracy because of the extraction of features in intricate detail, enhancing sensitivity and specificity. It smoothly integrates with clinical workflows to offer healthcare professionals actionable insights toward diagnosis and management of COPD. There is further potential in enhancement by integrating multimodal data like patient clinical history and genetic information. The system is also combined with an Arduino-based implementation with LCD display monitoring for real-time monitoring and user-friendly interaction. This would revolutionize COPD diagnosis, leading to earlier diagnosis, better patient outcomes, and smoother healthcare delivery.