RT info:eu-repo/semantics/article
T1 An effective method for lung cancer diagnosis from CT scan using deep learning-based support vector network
A1 Shafi, Imran
A1 Din, Sadia
A1 Khan, Asim
A1 Torre Díez, Isabel de la
A1 Pali Casanova, Ramón del Jesús
A1 Pifarre, Kilian Tutusaus
A1 Ashraf, Imran
K1 Lungs - Cancer
K1 Lungs - Diseases - Diagnosis
K1 Lungs - Cancer - Treatment
K1 Cancer - Diagnosis
K1 Pulmones - Cáncer
K1 Pulmones -  Cáncer -  Diagnóstico
K1 Radiology
K1 Tomografía computada
K1 Pulmones - Radiografia
K1 Oncology
K1 3201.01 Oncología
AB 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.
PB MDPI
SN 2072-6694
YR 2022
FD 2022
LK https://uvadoc.uva.es/handle/10324/60020
UL https://uvadoc.uva.es/handle/10324/60020
LA eng
NO Cancers, 2022, Vol. 4, Nº. 21, 5457
NO Producción Científica
DS UVaDOC
RD 20-may-2024