A Computer-Aided Diagnosis System for Lung Cancer Detection with Automatic Region Growing, Multistage Feature Selection and Neural Network Classifier
Manikandan T1, Devi B2, Helanvidhya T3
1Manikandan T, Professor, Department of ECE, Rajalakshmi Engineering College, Chennai (Tamil Nadu), India.
2Devi B, Assistant Professor, Department of ECE, Rajalakshmi Engineering College, Chennai (Tamil Nadu), India.
3Helanvidhya T, Assistant Professor, Department of ECE, Rajalakshmi Engineering College, Chennai (Tamil Nadu), India.
Manuscript received on 27 November 2019 | Revised Manuscript received on 07 December 2019 | Manuscript Published on 14 December 2019 | PP: 409-413 | Volume-9 Issue-1S November 2019 | Retrieval Number: A10811191S19/2019©BEIESP | DOI: 10.35940/ijitee.A1081.1191S19
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: An effective automatic region growing was developed in this work for the segmentation of suspected lung nodules from the Computed Tomography (CT) lung images. After the segmentation of the suspected lung nodules the eccentricity and area features were calculated to eliminate line like structures and tiny clusters below 3mm. The centroid analysis, contrast, autocorrelation and homogeneity features were extracted for the suspected lung nodules. The extracted features were trained and tested with Artificial Neural Network (ANN) to remove the blood vessels and calcifications (calcium deposition in the lungs). This work was carried out on 106 patients images retrospectively collected from Bharat Scans, Chennai, which had 56 cancerous nodules and 745 non-cancerous nodules (size greater than 3 mm). The proposed work yielded sensitivity, specificity and accuracy of 100%, 93% and 94%, respectively.
Keywords: Computed Tomography, CAD, Lung Cancer, Cluster, Morphology, Nodules.
Scope of the Article: Computer Science and Its Applications