Shafi, Imran and Din, Sadia and Khan, Asim and Díez, Isabel De La Torre and Pali-Casanova, Ramón and Tutusaus, Kilian and Ashraf, Imran UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, ramon.pali@unini.edu.mx, kilian.tutusaus@uneatlantico.es, UNSPECIFIED (2022) An Effective Method for Lung Cancer Diagnosis from CT Scan Using Deep Learning-Based Support Vector Network. Cancers, 14 (21). p. 5457. ISSN 2072-6694
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Abstract
The diagnosis of early-stage lung cancer is challenging due to its asymptomatic nature, especially given the repeated radiation exposure and high cost of computed tomography(CT). Examining the lung CT images to detect pulmonary nodules, especially the cell lung cancer lesions, is also tedious and prone to errors even by a specialist. This study proposes a cancer diagnostic model based on a deep learning-enabled support vector machine (SVM). The proposed computer-aided design (CAD) model identifies the physiological and pathological changes in the soft tissues of the cross-section in lung cancer lesions. The model is first trained to recognize lung cancer by measuring and comparing the selected profile values in CT images obtained from patients and control patients at their diagnosis. Then, the model is tested and validated using the CT scans of both patients and control patients that are not shown in the training phase. The study investigates 888 annotated CT scans from the publicly available LIDC/IDRI database. The proposed deep learning-assisted SVM-based model yields 94% accuracy for pulmonary nodule detection representing early-stage lung cancer. It is found superior to other existing methods including complex deep learning, simple machine learning, and the hybrid techniques used on lung CT images for nodule detection. Experimental results demonstrate that the proposed approach can greatly assist radiologists in detecting early lung cancer and facilitating the timely management of patients.
| Item Type: | Article |
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| Uncontrolled Keywords: | lung cancer detection; capsule neural network; wide network; computed tomography |
| Subjects: | Subjects > Biomedicine Subjects > Engineering |
| Divisions: | Europe University of Atlantic > Research > Scientific Production Fundación Universitaria Internacional de Colombia > Research > Scientific Production Ibero-american International University > Research > Scientific Production Ibero-american International University > Research > Scientific Production Universidad Internacional do Cuanza > Research > Scientific Production |
| Depositing User: | Sr Bibliotecario |
| Date Deposited: | 16 Nov 2022 10:25 |
| Last Modified: | 11 Jul 2023 07:12 |
| URI: | http://repositorio.funiber.org/id/eprint/4611 |
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