Lung Cancer Detecting using Radiomics Features and Machine Learning Algorithm

Title

Lung Cancer Detecting using Radiomics Features and Machine Learning Algorithm

Creator

Rajesh M.N.; Tanni N.; Baig Z.T.

Description

Lung Cancer Incidence across the globe is the second leading cancer type tallying to about 2,206,771 during 2020 and is estimated to rise to about 3,503,378 by 2040 for both male and female sexes and for all ages accounting to 11.4% as per Globocan 2020 [1]. It is the leading death-causing cancer. Lung Cancer [2] in broad terms encompasses Trachea, bronchus as well as lungs. Purpose: The study is aimed to understand Radiomics based approach in the identification as well as classification of CT Images with Lung Cancer when Machine Learning (ML) algorithms are applied. Method: CT Image from LIDC-IDRI [4] Dataset has been chosen. CT Image Dataset was balanced and image features by PyRadiomics library were collected. Various ML features classification algorithms are utilized to create models and matrices adopted in judging their accuracies. The models, distinctive capacity is assessed by receiver operating characteristics (ROC) analysis. Result: The Accuracy scores and ROC-AUC values obtained for various Classification Model are as follows, for Ada Boosting, the accuracy score was 0.9993 ROC-AUC was 0.9993 and followed by GBM, the accuracy score was 0.9993, was 0.9992. Conclusion: Extracting texture parameters on CT images as well as linking the Radiomics method with ML would categorize Lung Cancer commendably. 2023 IEEE.

Source

Proceedings - International Conference on Technological Advancements in Computational Sciences, ICTACS 2023, pp. 1479-1485.

Date

2023-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Feature Extraction; Image Classification; Lung Cancer; Machine Learning; Radiomics; Selection

Coverage

Rajesh M.N., Jain (Deemed-to Be-University), Electronics and Communication Engineering, Bengaluru, India; Tanni N., Christ (Deemed to Be University), B.Tech in Computer Science and Engineering (Data Science), Bengaluru, India; Baig Z.T., Amity University, Dept of It, Taskent, Uzbekistan

Rights

Restricted Access

Relation

ISBN: 979-835034233-8

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/ICTACS59847.2023.10389900

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