Slide-Detect: An Accurate Deep Learning Diagnosis of Lung Infiltration

Lung infiltration is a non-communicable condition where materials with higher density than air exist in the parenchyma tissue of the lungs. Lung infiltration can be hard to be detected in an X-ray scan even for a radiologist, especially at the early stages making it a leading cause of death. In resp...

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Bibliographic Details
Published in:Data intelligence 2023-11, Vol.5 (4), p.1048-1062
Main Authors: Mohamed, Ahmed E., Fayek, Magda B., Farouk, Mona
Format: Article
Language:English
Subjects:
Artificial neural networks
Classification
Datasets
Deep learning
Infiltration
Lung cancer
Lung diseases
Lungs
Machine learning
Methods
Neural networks
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Summary:Lung infiltration is a non-communicable condition where materials with higher density than air exist in the parenchyma tissue of the lungs. Lung infiltration can be hard to be detected in an X-ray scan even for a radiologist, especially at the early stages making it a leading cause of death. In response, several deep learning approaches have been evolved to address this problem. This paper proposes the Slide-Detect technique which is a Deep Neural Networks (DNN) model based on Convolutional Neural Networks (CNNs) that is trained to diagnose lung infiltration with Area Under Curve (AUC) up to 91.47%, accuracy of 93.85% and relatively low computational resources.
ISSN:2641-435X
2641-435X
DOI:10.1162/dint_a_00233