Open AccessDesign and Development of an Efficient Mining Framework for Pre-Cancerous Lesion Detection in Lung Using Non-Invasive CT Imaging
Author(s) -
R. Priyatharshini,
K Aparajitha.,
B. Aarthi,
N. Dhivya
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.a5265.119119
Subject(s) - segmentation , context (archaeology) , computer science , lung , artificial intelligence , image segmentation , lung disease , region of interest , lung cancer , medical imaging , computer vision , pattern recognition (psychology) , radiology , medicine , pathology , paleontology , biology
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 imaging is a challenge, since the images are varied, complex and can contain irregular shapes with noisy values.In this context, the segmentation of the Region of Interest from Lung CT and detecting the pre-cancerous lesions is an important research problem that is receiving growing attention. Hence an efficient methodology on ACM based automatic segmentation and precancerous lesion detection is proposed.