This paper presents DeepCOPD, an innovative deep learning approach for accurate detection of Chronic Obstructive Pulmonary Disease (COPD) using respiratory sound analysis. The proposed approach utilizes a Convolutional Neural Network (CNN) model trained on a respiratory sound database containing wheezes, crackles, and both crackles and wheezes. To overcome the challenge of a small dataset, innovative techniques such as device-specific fine-tuning, concatenation-based augmentation, blank region clipping, and smart padding are employed. These techniques enable efficient utilization of the dataset, resulting in an impressive accuracy of 90% to 95%. The implementation includes an app with a user-friendly interface developed using HTML, CSS, Flask, and Heroku. By leveraging deep learning and respiratory sound analysis, the app offers a promising solution for accurate COPD detection, providing significant advancements in respiratory health monitoring. Users can upload respiratory sound audios to the app for COPD detection, enhancing early diagnosis and improving patient outcomes.