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A Hybrid Approach for the Lung(s) Nodule Detection Using the Deformable Model and Distance Transform

Ayyaz Hussain1, Mohammed Alawairdhi2, Fayez Alazemi3, Sajid Ali Khan4, Muhammad Ramzan2,*

1 Department of Computer Science, Quaid e Azam University, Islamabad, 44000, Pakistan
2 College of Computing and Informatics, Saudi Electronic University, Riyadh, 11673, Saudi Arabia
3 Public Authority for Applied Education and Training, 6500, Kuwait
4 Department of Software Engineering, Foundation University Islamabad, Islamabad, 44000, Pakistan

* Corresponding Author: Muhammad Ramzan. Email: email

Intelligent Automation & Soft Computing 2020, 26(5), 857-871. https://doi.org/10.32604/iasc.2020.010120

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.

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Cite This Article

A. Hussain, M. Alawairdhi, F. Alazemi, S. Ali Khan and M. Ramzan, "A hybrid approach for the lung(s) nodule detection using the deformable model and distance transform," Intelligent Automation & Soft Computing, vol. 26, no.5, pp. 857–871, 2020.



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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