Development of CAD System for Automatic Lung Nodule Detection: A Review

Sari, Sekar and Soesanti, Indah and Setiawan, Noor Akhmad (2021) Development of CAD System for Automatic Lung Nodule Detection: A Review. In: International Conference on Bioinformatics, Biotechnology, and Biomedical Engineering, BioMIC 2021.

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Abstract

Lung cancer is a type of cancer that spreads rapidly and is the leading cause of mortality globally. The Computer-Aided Detection (CAD) system for automatic lung cancer detection has a significant influence on human survival. In this article, we report the summary of relevant literature on CAD systems for lung cancer detection. The CAD system includes preprocessing techniques, segmentation, lung nodule detection, and false-positive reduction with feature extraction. In evaluating some of the work on this topic, we used a search of selected literature, the dataset used for method validation, the number of cases, the image size, several techniques in nodule detection, feature extraction, sensitivity, and false-positive rates. The best performance CAD systems of our analysis results show the sensitivity value is high with low false positives and other parameters for lung nodule detection. Furthermore, it also uses a large dataset, so the further systems have improved accuracy and precision in detection. CNN is the best lung nodule detection method and need to develop, it is preferable because this method has witnessed various growth in recent years and has yielded impressive outcomes. We hope this article will help professional researchers and radiologists in developing CAD systems for lung cancer detection. © The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0/).

Item Type: Conference or Workshop Item (Paper)
Additional Information: Cited by: 1; Conference name: 4th International Conference on Bioinformatics, Biotechnology, and Biomedical Engineering, BioMIC 2021; Conference date: 6 October 2021 through 7 October 2021; Conference code: 192780; All Open Access, Gold Open Access
Subjects: T Technology > T Technology (General)
T Technology > T Technology (General) > Technical education. Technical schools
Divisions: Faculty of Engineering > Electronics Engineering Department
Depositing User: Sri JUNANDI
Date Deposited: 25 Oct 2024 07:18
Last Modified: 25 Oct 2024 07:18
URI: https://ir.lib.ugm.ac.id/id/eprint/5614

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