Pleural Effusion Detection Using Machine Learning and Deep Learning Based on Computer Vision
Document Type
Book
Publication Date
2022
Abstract
Pleural effusion is one of the serious chest diseases that affect humanlife depending on the causes, such as malignant tumors, liver, kidney or chestfailure, and the risks increase with the delay in diagnosis and treatment. In recentyears, artificial intelligence (AI) has achieved a significant development in themedical field. As part of artificial intelligence, deep learning (DL), machine learn-ing (ML), and computer vision (CV) have become very important in the diagnosisand treatment of diseases, as they help in terms of early and accurate diseases diag-nosis and suggesting the best treatments. Furthermore, with the widespread useof medical images in diagnosing diseases, the need for computer vision, machinelearning, and deep learning to analyze and understand those images, and to helpclinicians make quick and accurate diagnoses has increased. In this paper, machinelearning model i.e., artificial neural network (ANN), and deep learning models i.e.,AlexNet, GoogleNet, SqueezeNet, and DarkNet19, are used to detect the presenceor absence of pleural effusion in Chest X-ray14 dataset images. We have used 80%of the dataset for training the models, and the remaining 20% for testing (PDF) Pleural Effusion Detection Using Machine Learning and Deep Learning Based on Computer Vision.
Recommended Citation
yhiea, Nashwa Mohamed, "Pleural Effusion Detection Using Machine Learning and Deep Learning Based on Computer Vision" (2022). Basic Science. 6.
https://buescholar.bue.edu.eg/basic_science/6