The DOI matches its Crossref record.
Optimal deep learning model for classification of lung cancer on CT images
Abstract
Abstract Lung cancer is one of the dangerous diseases that cause huge cancer death worldwide. Early detection of lung cancer is the only possible way to improve a patient’s chance for survival. A Computed Tomography (CT) scan used to find the position of tumor and identify the level of cancer in the body. The current study presents an innovative automated diagnosis classification method for Computed Tomography (CT) images of lungs. In this paper, the CT scan of lung images was analyzed with the assistance of Optimal Deep Neural Network (ODNN) and Linear Discriminate Analysis (LDA). The deep features extracted from a CT lung images and then dimensionality of feature is reduced using LDR to classify lung nodules as either malignant or benign. The ODNN is applied to CT images and then, optimized using Modified Gravitational Search Algorithm (MGSA) for identify the lung cancer classification The comparative results show that the proposed classifier gives the sensitivity of 96.2%, specificity of 94.2% and accuracy of 94.56%.
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Cited by
Rahimzadeh, M., & Attar, A. (2020) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2020.100360 · Cited by 695
Deep neural network correlation learning mechanism for CT brain tumor detection
Woźniak, M., Siłka, J., & Wieczorek, M. (2023) · Neural Computing and Applications · doi:10.1007/s00521-021-05841-x · Cited by 284
Metadata
- Authors
- S.K., L., Mohanty, S. N., K., S., N., A., & Ramirez, G.
- Venue
- Future Generation Computer Systems
- Publisher
- Elsevier BV
- Published
- 2019-03
- Volume
- 92
- Pages
- 374-382
- ISSN
- 0167-739X (ISSN-L)
- OpenAlex
- W2897821359
- Cited by
- 540
- References
- 0
- FWCI
- 23.91
- Type
- journal article
- Language
- en
- Open access
- closed
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- S.K., L., Mohanty, S. N., K., S., N., A., & Ramirez, G. (2019). Optimal deep learning model for classification of lung cancer on CT images. Future Generation Computer Systems. https://doi.org/10.1016/j.future.2018.10.009
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