Leveraging Machine Learning for Early Detection of Lung Diseases

TL;DR

This paper explores combining traditional image processing with deep learning models to enable rapid, accurate, and non-invasive early detection of lung diseases like COVID-19, lung cancer, and pneumonia from chest x-rays, especially in resource-limited settings.

Contribution

It introduces a hybrid approach that integrates traditional image processing with advanced neural networks for improved lung disease diagnosis.

Findings

Deep learning models achieved high accuracy, precision, recall, and F1 scores.

CNNs, VGG16, InceptionV3, and EfficientNetB0 demonstrated reliable diagnostic performance.

The approach offers scalable, non-invasive diagnostic solutions for respiratory diseases.

Abstract

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.