@Article{iasc.2020.010120,
AUTHOR = {Ayyaz Hussain, Mohammed Alawairdhi, Fayez Alazemi, Sajid Ali Khan, Muhammad Ramzan},
TITLE = {A Hybrid Approach for the Lung(s) Nodule Detection Using the Deformable  Model and Distance Transform},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {26},
YEAR = {2020},
NUMBER = {5},
PAGES = {857--871},
URL = {http://www.techscience.com/iasc/v26n5/40809},
ISSN = {2326-005X},
ABSTRACT = {The Computer Aided Diagnosis (CAD) systems are gaining more 
recognition and being used as an aid by clinicians for detection and interpretation 
of diseases every passing day due to their increasing accuracy and reliability. The 
lung(s) nodule detection is a very crucial and difficult step for CAD systems. In 
this paper, a hybrid approach for the lung nodule detection using a deformable 
model and distance transform has been proposed. The proposed method has the 
ability to detect all major kinds of nodules such as the juxta-plueral, isolated, and 
the juxta-vescular, along with the non-solid nodules automatically and 
intelligently. Results show an impressive 95.2% accuracy with 4.85 false positives 
per scan. One significant achievement of the proposed work is its ability to detect 
various sizes of nodules from 3 mm to 30 mm. The proposed technique has been 
tested on the publicly available Lung(s) Image Database Consortium (LIDC). The 
results clearly show the effectiveness of the proposed technique in early detection 
with impressive accuracy.},
DOI = {10.32604/iasc.2020.010120}
}