Abstract
This project is based upon the different process determined for the recognition of lung tuberculosis using different modalities like filtering, lung boundaries detection, extraction of tuberculosis and performance evaluation measures. We all basically know about lung tuberculosis, which is caused by a bacterial infection that causes the majority of death rate around the world. It is caused by Mycobacterium tuberculosis (M tuberculosis). This project tries to detect the tuberculosis in minimum investment. In this project first we get the X-ray images of infected person. After that the X-ray images are given as the input then the preprocessing is applied in that we use filter which removes the unnecessary noise and also helps to acquire clear images. Then the output which is obtained from the preprocessing is given as the as the input for lung boundary detection after detecting the boundaries the output we detect is fused to get Lung Boundary Detection. In this LBD the features such as area, major and minor axis etc...are detected. Under the various process we detect the TB that is present in X-ray or not.
Export citation and abstract BibTeX RIS
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
This article (and all articles in the proceedings volume relating to the same conference) has been retracted by IOP Publishing following an extensive investigation in line with the COPE guidelines. This investigation has uncovered evidence of systematic manipulation of the publication process and considerable citation manipulation.
IOP Publishing respectfully requests that readers consider all work within this volume potentially unreliable, as the volume has not been through a credible peer review process.
IOP Publishing regrets that our usual quality checks did not identify these issues before publication, and have since put additional measures in place to try to prevent these issues from reoccurring. IOP Publishing wishes to credit anonymous whistleblowers and the Problematic Paper Screener [1] for bringing some of the above issues to our attention, prompting us to investigate further.
[1] Cabanac G, Labbé C and Magazinov A 2021 arXiv:2107.06751v1
Retraction published: 23 February 2022