Automated Detection of Pulmonary Diseases Using Deep Learning on Chest X-ray Images

Abstract

Pulmonary diseases, like asthma, TB, and lung cancer, are still big problems in world health, and they cause a lot of deaths and illnesses. Early and correct identification is very important for treatment to work and for patients to have better results. Traditional ways of diagnosing, which mostly depend on doctor analysis of chest X-rays, take a long time and can be flawed by human mistake. Deep learning has become a strong tool in medical imaging in recent years. It could be used to automatically find diseases with a high level of accuracy. This essay gives a thorough look at how deep learning methods can be used to automatically find lung diseases in chest X-ray pictures. We created a convolutional neural network (CNN) design that works perfectly for looking at chest X-rays. The model was trained and tested on a big, freely available dataset with thousands of tagged pictures showing a wide range of lung diseases. Our method is based on making the network's design work better so that it can take more features and make classifications more accurate while still using as little computing power as possible. We used data enrichment methods and transfer learning from pre-trained models to get around the problem of not having enough labeled data. This made the model much better at generalization. Several performance measures, such as accuracy, precision, recall, and F1-score, were used to carefully test the CNN model. From the results, we saw that our model was very good at finding a number of lung diseases from chest X-rays, better than both standard methods and some of the newest models. We also used AI methods that can be explained to show doctors how the model made decisions visually. This helped them understand and trust the results of the automatic system. For AI-based systems to be used in healthcare settings, where dependability and ease of interpretation are very important, they need to be clear.