An optimal model for detection of lung cancer using convolutional neural network
| Dublin Core | PKP Metadata Items | Metadata for this Document | |
| 1. | Title | Title of document | An optimal model for detection of lung cancer using convolutional neural network |
| 2. | Creator | Author's name, affiliation, country | Kavitha Belegere Chandraiah; Adichunchanagiri University; India |
| 2. | Creator | Author's name, affiliation, country | Naveen Kalenahalli Bhoganna; Adichunchanagiri University; India |
| 3. | Subject | Discipline(s) | |
| 3. | Subject | Keyword(s) | Benign; CNN lung cancer; Computed tomography; Malignant; Mean average precision |
| 4. | Description | Abstract | In terms of frequency and mortality, lung cancer ranks second among all cancers worldwide for both men and women. It is suggested that pattern classification and machine learning be applied to the identification and categorization of lung cancer. Convolution neural network (CNN) techniques divide the input data into groups according to the distinctive characteristics of the input. Using a standard approach to analyze a large number of computed tomography images, early detection of lung cancer can save lives. The suggested effort is centered on identifying the precise type of cancer and making predictions about whether it is benign or aggressive. The deployment of proposed model is an attempt to improve the accuracy of the system. The proposed work showed an overall accuracy of 98.4% during the detection of lung cancer and 98.8% accuracy towards the prediction of specific type in the lung cancer. Mean average precision score of 97.17% and 98.75% test and validation respectively. 0.96, 0.93, and 0.95 for malignant test data. |
| 5. | Publisher | Organizing agency, location | Institute of Advanced Engineering and Science |
| 6. | Contributor | Sponsor(s) | |
| 7. | Date | (YYYY-MM-DD) | 2024-04-01 |
| 8. | Type | Status & genre | Peer-reviewed Article |
| 8. | Type | Type | |
| 9. | Format | File format | |
| 10. | Identifier | Uniform Resource Identifier | https://ijeecs.iaescore.com/index.php/IJEECS/article/view/33954 |
| 10. | Identifier | Digital Object Identifier (DOI) | http://doi.org/10.11591/ijeecs.v34.i1.pp134-143 |
| 11. | Source | Title; vol., no. (year) | Indonesian Journal of Electrical Engineering and Computer Science; Vol 34, No 1: April 2024 |
| 12. | Language | English=en | en |
| 14. | Coverage | Geo-spatial location, chronological period, research sample (gender, age, etc.) | |
| 15. | Rights | Copyright and permissions |
Copyright (c) 2024 Institute of Advanced Engineering and Science This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |