A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf
.
Design and Development of an Efficient Mining Framework for Pre-Cancerous Lesion Detection in Lung using Non-Invasive CT Imaging
2019
VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE
The ability to analyze and identify meaningful patterns in clinical data must be addressed to provide a better understanding of disease. Currently existing solutions for disease diagnosis systems are costly, time consuming and prone to errors, due to the diversity of medical information sources. Lung Disease Diagnosis individual is based on medical images (Lung CTs) includes Lung segmentation, and the detection of cancerous lesions in the Lung. Segmenting the region of interest from medical
doi:10.35940/ijitee.a5265.119119
fatcat:ijrobx3mobcnrg6fkfzsvx6mty