A hybrid approach of data visualization technique and random forest classifier for binary classification of lung CT images

DOI

10.2991/978-94-6463-529-4_21How to use a DOI?

Keywords

lung cancer; machine learning; random forest; binary classification; computed tomography

Abstract

Lung cancer is the most common and dangerous cancer worldwide. An automatic detection system is the need of the hour for early diagnosis. Machine learning classifiers often encounter outliers in the extracted features. Motivated by this, the main aim of this study is to develop an outlier free automated computer-aided system for the prediction of pulmonary nodules using various data visualization and machine learning (ML) classification techniques that can help in the decision-making process of radiologists. Data visualization techniques are used for removing outlier values from the extracted features. A comparative analysis using several ML techniques such as Decision Tree, Support Vector Machine, K Nearest Neighbours, and Random Forest classifier has been performed. Random forest is the best-performing classifier, which obtained 92.92% cross-validation accuracy, 96% precision, 90.74% sensitivity, 95.56% specificity, and 93.29% F1 score. Hence, the proposed model can open up new opportunities for radiologists for early lung cancer detection.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Ananya Bhattacharjee
AU  - P. Stoila Cindy
AU  - R. Murugan
AU  - Tripti Goel
PY  - 2024
DA  - 2024/10/04
TI  - A hybrid approach of data visualization technique and random forest classifier for binary classification of lung CT images
BT  - Proceedings of the International Conference on Signal Processing and Computer Vision (SIPCOV-2023)
PB  - Atlantis Press
SP  - 231
EP  - 243
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-529-4_21
DO  - 10.2991/978-94-6463-529-4_21
ID  - Bhattacharjee2024
ER  -