Leveraging Machine Learning for Early Detection of Lung Diseases

• URL: http://arxiv.org/abs/2512.23757v1
• Date: Sat, 27 Dec 2025 16:50:23 GMT
• Title: Leveraging Machine Learning for Early Detection of Lung Diseases
• Authors: Bahareh Rahmani, Harsha Reddy Bindela, Rama Kanth Reddy Gosula, Krishna Yedubati, Mohammad Amir Salari, Leslie Hinyard, Payam Norouzzadeh, Eli Snir, Martin Schoen,
• Abstract summary: This study offers rapid, accurate, and non-invasive diagnostic solutions that can significantly impact patient outcomes.<n>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.<n>We trained and validated various neural network models, including CNNs, VGG16, InceptionV3, and EfficientNetB0, with high accuracy, precision, recall, and F1 scores.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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