@inproceedings{12b89cf5d53a4687b930520ed1d3f971,
title = "Exploring the effects of compression via principal components analysis on X-ray image cassification",
abstract = "Image compression in medical applications implores careful consideration of the effects on data veracity. The inexorable challenge of assessing the volume-veracity trade-off is becoming more prevalent in this critical application area, and particularly when machine learning is used for the purpose of assisted diagnostics. This paper investigates the impact of compressing X-ray images on the accuracy of fracture diagnostics. The accuracy of the classification system is assessed for X-ray images of both healthy and fracture bones when subjected to different levels of compression. Compression is achieved using principal components analysis. Results indicate that accuracy is only marginally affected under a level one compression but begins to deteriorate under level two compression. These results are potentially useful as the level one compression yields gains up to 94% with less than a 2% drop in classification accuracy.",
keywords = "Big data, Compression, Image classification, Principal component analysis, X-ray",
author = "Vikash Rameshar and Wesley Doorsamy",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 6th International Conference on Soft Computing and Machine Intelligence, ISCMI 2019 ; Conference date: 19-11-2019 Through 20-11-2019",
year = "2019",
month = nov,
doi = "10.1109/ISCMI47871.2019.9004301",
language = "English",
series = "2019 6th International Conference on Soft Computing and Machine Intelligence, ISCMI 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "150--154",
booktitle = "2019 6th International Conference on Soft Computing and Machine Intelligence, ISCMI 2019",
address = "United States",
}