Combined Unsupervised and Supervised Learning for Improving Chest X-Ray Classification

Anca Ignat, Robert-Adrian Găină

2023

Abstract

This paper studies the problem of pneumonia classification of chest X-ray images. We first apply clustering algorithms to eliminate contradictory images from each of the two classes (normal and pneumonia) of the dataset. We then train different classifiers on the reduced dataset and test for improvement in performance evaluators. For feature extraction and also for classification we use ten well-known Convolutional Neural Networks (Resnet18, Resnet50, VGG16, VGG19, Densenet, Mobilenet, Inception, Xception, InceptionResnet and Shufflenet). For clustering, we employed 2-means, agglomerative clustering and spectral clustering. Besides the above-mentioned CNN, linear SVMs (Support Vector Machines) and Random Forest (RF) were employed for classification. The tests were performed on Kermany dataset. Our experiments show that this approach leads to improvement in classification results.

Paper Citation

in Harvard Style

Ignat A. and Găină R. (2023). Combined Unsupervised and Supervised Learning for Improving Chest X-Ray Classification. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP; ISBN 978-989-758-634-7, SciTePress, pages 475-482. DOI: 10.5220/0011793000003417

in Bibtex Style

@conference{visapp23,
author={Anca Ignat and Robert-Adrian Găină},
title={Combined Unsupervised and Supervised Learning for Improving Chest X-Ray Classification},
booktitle={Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP},
year={2023},
pages={475-482},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011793000003417},
isbn={978-989-758-634-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP
TI - Combined Unsupervised and Supervised Learning for Improving Chest X-Ray Classification
SN - 978-989-758-634-7
AU - Ignat A.
AU - Găină R.
PY - 2023
SP - 475
EP - 482
DO - 10.5220/0011793000003417
PB - SciTePress