An approach for classification of lung nodules

¹ Department of Computer Science Engineering, SJB Institute of Technology, Bangalore, Affiliated to Visvesvaraya Technological University, Belagavi, Karnataka, India

² Department of Master Computer Application, JSSTU, Mysuru, Affiliated to JSS Science and Technology University, Mysuru, Karnataka, India

Tumor Discovery 2023, 2(1), 317 https://doi.org/10.36922/td.317

Submitted: 28 December 2022 | Accepted: 17 February 2023 | Published: 8 March 2023

© 2023 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )

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Abstract

The main objective of the proposed work is to develop an automated computer-aided detection (CAD) system to classify lung nodules using various classifiers from computed tomography (CT) images. One of the most important steps in lung nodule detection is the classification of nodule and non-nodule patterns in CT. The early detection of the condition helps lower the mortality rate. The developed CAD systems consist of segmentation, feature extraction, and classification. In this work, a filter method is used to segment the infected region. Later, we extracted features through and fed into classifiers such as Decision Stump (DS), Random Forest (RF), and Back Propagation Neural Network (BPNN). The experimentation was conducted on LIDC-IDRI dataset, and the results with BPNN outperformed those with DS and RF classifiers.

Keywords

Decision stump

Random forest

AdaBoost-Decision stump

AdaBoost-Random forest

Back propagation neural network

Funding

None.

Conflict of interest

The authors declare no conflict of interest.

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