Leveraging Machine Learning for Early Detection of Lung Diseases
This work addresses the problem of limited access to radiologists in healthcare, offering a potential tool for improving patient outcomes, though it appears incremental in applying existing methods to medical imaging.
The study tackled early detection of lung diseases from chest x-rays using deep learning models, achieving high accuracy, precision, recall, and F1 scores to provide rapid and non-invasive diagnostic solutions.
A combination of traditional image processing methods with advanced neural networks concretes a predictive and preventive healthcare paradigm. This study offers rapid, accurate, and non-invasive diagnostic solutions that can significantly impact patient outcomes, particularly in areas with limited access to radiologists and healthcare resources. In this project, deep learning methods apply in enhancing the diagnosis of respiratory diseases such as COVID-19, lung cancer, and pneumonia from chest x-rays. We trained and validated various neural network models, including CNNs, VGG16, InceptionV3, and EfficientNetB0, with high accuracy, precision, recall, and F1 scores to highlight the models' reliability and potential in real-world diagnostic applications.