A review of convolutional neural network-based computer-aided lung nodule detection system
Worldwide, lung cancer is the major cause of death and rapidly spreads. Lung tissue that is benign does not grow significantly, but lung tissue that is malignant grows rapidly and attacks the body, posing a grave threat to one's health. This paper provides a literature review of computer-aided...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Institute of Advanced Engineering and Science
2023
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| Subjects: | |
| Online Access: | https://repository.ugm.ac.id/285897/1/A%20review%20of%20convolutional%20neural%20network.pdf |
| _version_ | 1826050846409883648 |
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| author | Sari, Sekar Sutikno, Tole Soesanti, Indah Setiawan, Noor Akhmad |
| author_facet | Sari, Sekar Sutikno, Tole Soesanti, Indah Setiawan, Noor Akhmad |
| author_sort | Sari, Sekar |
| collection | UGM |
| description | Worldwide, lung cancer is the major cause of death and rapidly spreads. Lung tissue that is benign does not grow significantly, but lung tissue that is malignant grows rapidly and attacks the body, posing a grave threat to one's health. This paper provides a literature review of computer-aided detection (CAD) systems for lung cancer diagnosis. Preprocessing, segmentation, detection, and classification are the stages of the CAD system. This review divides the preprocessing into three stages: image smoothing, edge sharpening, and noise removal. Additionally, lung segmentation is divided into three stages: histogram-based thresholding, linked component analysis, and lung extraction. The detecting phase aids in decreasing the workload. Several techniques are briefly described, including random forest, naive bayes, k-nearest neighbor (k-NN), support vector machine (SVM), and convolutional neural network (CNN). Classification is the final stage; the image is then identified as containing or not possessing nodules. The prospect of incorporating CNN-based deep learning techniques into the CAD system is discussed. This paper is superior to other review studies on this topic due to its comprehensive examination of pertinent literature and structured presentation. We hope that our research may help professional researchers and radiologists design more effective CAD systems for lung cancer detection. |
| first_indexed | 2024-03-14T00:11:05Z |
| format | Article |
| id | oai:generic.eprints.org:285897 |
| institution | Universiti Gadjah Mada |
| language | English |
| last_indexed | 2024-03-14T00:11:05Z |
| publishDate | 2023 |
| publisher | Institute of Advanced Engineering and Science |
| record_format | dspace |
| spelling | oai:generic.eprints.org:2858972024-03-04T04:22:20Z https://repository.ugm.ac.id/285897/ A review of convolutional neural network-based computer-aided lung nodule detection system Sari, Sekar Sutikno, Tole Soesanti, Indah Setiawan, Noor Akhmad Electrical and Electronic Engineering not elsewhere classified Worldwide, lung cancer is the major cause of death and rapidly spreads. Lung tissue that is benign does not grow significantly, but lung tissue that is malignant grows rapidly and attacks the body, posing a grave threat to one's health. This paper provides a literature review of computer-aided detection (CAD) systems for lung cancer diagnosis. Preprocessing, segmentation, detection, and classification are the stages of the CAD system. This review divides the preprocessing into three stages: image smoothing, edge sharpening, and noise removal. Additionally, lung segmentation is divided into three stages: histogram-based thresholding, linked component analysis, and lung extraction. The detecting phase aids in decreasing the workload. Several techniques are briefly described, including random forest, naive bayes, k-nearest neighbor (k-NN), support vector machine (SVM), and convolutional neural network (CNN). Classification is the final stage; the image is then identified as containing or not possessing nodules. The prospect of incorporating CNN-based deep learning techniques into the CAD system is discussed. This paper is superior to other review studies on this topic due to its comprehensive examination of pertinent literature and structured presentation. We hope that our research may help professional researchers and radiologists design more effective CAD systems for lung cancer detection. Institute of Advanced Engineering and Science 2023 Article PeerReviewed application/pdf en https://repository.ugm.ac.id/285897/1/A%20review%20of%20convolutional%20neural%20network.pdf Sari, Sekar and Sutikno, Tole and Soesanti, Indah and Setiawan, Noor Akhmad (2023) A review of convolutional neural network-based computer-aided lung nodule detection system. IAES International Journal of Artificial Intelligence, 12 (3). pp. 1044-1061. ISSN 20894872 https://ijai.iaescore.com/index.php/IJAI/article/view/22667 10.11591/ijai.v12.i3.pp1044-1061 |
| spellingShingle | Electrical and Electronic Engineering not elsewhere classified Sari, Sekar Sutikno, Tole Soesanti, Indah Setiawan, Noor Akhmad A review of convolutional neural network-based computer-aided lung nodule detection system |
| title | A review of convolutional neural network-based computer-aided lung nodule detection system |
| title_full | A review of convolutional neural network-based computer-aided lung nodule detection system |
| title_fullStr | A review of convolutional neural network-based computer-aided lung nodule detection system |
| title_full_unstemmed | A review of convolutional neural network-based computer-aided lung nodule detection system |
| title_short | A review of convolutional neural network-based computer-aided lung nodule detection system |
| title_sort | review of convolutional neural network based computer aided lung nodule detection system |
| topic | Electrical and Electronic Engineering not elsewhere classified |
| url | https://repository.ugm.ac.id/285897/1/A%20review%20of%20convolutional%20neural%20network.pdf |
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