Lung Disease Detection using Deep Learning

doi:10.1109/ICET56601.2022.10004651

Paper

Lung Disease Detection using Deep Learning

Published Nov 29, 2022 · S. Bukhari, L. Fahad

2022 17th International Conference on Emerging Technologies (ICET)

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Abstract

Lung diseases refer to many disorders affecting the lungs, such as pneumonia, tuberculosis, lung cancer, and many other breathing problems. Due to COPD (chronic obstructive pulmonary disease), 3 million people die from this disease each year and it is the third main leading cause of human death worldwide. Early disease detection is important to take timely preventive measures. These diseases can be identified through CT and chest X-ray images of the lungs. The accurate detection of lung diseases is challenging due to high similarities between different lung diseases and variations in a single disease. The proposed work is focused on learning the less inter-class and high intra-class variations in different lung diseases. In the proposed approach, pre-processing, data augmentation and deep learning is used for classification of four categories including normal, lung cancer, pneumonia, and tuberculosis. The representation of minority classes is improved by using data augmentation technique DCGAN. A comprehensive evaluation of the proposed approach using a publicly available dataset demonstrates a better performance of the proposed approach compared to the existing approaches.

Study Snapshot

Key takeawayThe proposed deep learning approach accurately detects lung diseases, such as pneumonia, tuberculosis, and COPD, with better performance than existing approaches.

PopulationOlder adults (50-71 years)

Sample size24

MethodsObservational

OutcomesBody Mass Index projections

ResultsSocial networks mitigate obesity in older groups.

Lung Disease Detection using Deep Learning

References

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