Tropical Skin Disease Classification using Connected Attribute Filters

Fred Kiwanuka, Omar Abuelmaatti, Anang Amin, Brian Mukwaya

Abstract

Morphological connected filters operate on an image through flat zones which comprise the largest connected components with a constant signal. These filters identify and ultimately extract the whole connected components in an image without alteration of their boundaries and thus shape preserving. This is a desirable property in many image processing and analysis applications. However, due to the variability of the number of connected components, even in the case of images of the same resolution and size, their application in classification tasks has been limited. In this study, we propose an approach that computes the shape and size features of connected components and use these features for the classification of bacterial and viral tropical skin infections. We demonstrate the performance of the approach using gradient boosting machines and compare the results to deep learning approaches. Results show that the performance of our approach is comparable to that of Convolutional Neural Networks (CNN) based approach when trained on 1460 images. Moreover, CNN was pre-trained and required augmentation to achieve that perfomance. However, our approach is at least 56% faster than CNN.

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Paper Citation


in Harvard Style

Kiwanuka F., Abuelmaatti O., Amin A. and Mukwaya B. (2021). Tropical Skin Disease Classification using Connected Attribute Filters.In Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, ISBN 978-989-758-488-6, pages 338-345. DOI: 10.5220/0010203403380345


in Bibtex Style

@conference{visapp21,
author={Fred Kiwanuka and Omar Abuelmaatti and Anang Amin and Brian Mukwaya},
title={Tropical Skin Disease Classification using Connected Attribute Filters},
booktitle={Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,},
year={2021},
pages={338-345},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010203403380345},
isbn={978-989-758-488-6},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,
TI - Tropical Skin Disease Classification using Connected Attribute Filters
SN - 978-989-758-488-6
AU - Kiwanuka F.
AU - Abuelmaatti O.
AU - Amin A.
AU - Mukwaya B.
PY - 2021
SP - 338
EP - 345
DO - 10.5220/0010203403380345