Paper Title
Lung Tumor Segmentation Using U-Net Architecture
Article Identifiers
: Authors
Keywords
Lung tumor, U-Net, Convolution Neural Networks.
Abstract
Lung cancer screening based on Low-Dose CT (LDCT) has broadly implemented for the effectiveness and fast execution. Radiologists who worked with highest LDCT conceal image face for more confrontation, despite tedious labour and robotic repetition, the simple deletion of minor nodules, the absence of uniform criteria, etc. This calls for an appropriate strategy to aid radiologists in raising the observing the Nodule precision for effectiveness, affordability. The novel-based Deep Neural Network Systems have the potential to be used in the approach for detecting lung nodules. However, the successfulness for Hospitalized practice has not successfully recognized. The use for developing and estimate a Deep Learning Algorithm (DL) in Recognizing Pulmonary Nodules (PNs) for LDCT and investigate prevalence of Pulmonary nodules in China. Protocol with Reference Standard and Deep Learning Algorithm for finding Positive Nodules as researches done in Bland-Altman Examination. Lung Nodule Analysis (LUNA) is a Database which was available publically for the outermost Examination. The frequency of NCPNs also instigated and also different information about number, location, characteristics of pulmonary nodules from two radiologists.
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How To Cite (APA)
Madhushree R, Dr. Prabha R, & Dr. Asha (December-2022). Lung Tumor Segmentation Using U-Net Architecture. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 7(12), c9-c14. https://ijnrd.org/papers/IJNRD2212202.pdf
Issue
Volume 7 Issue 12, December-2022
Pages : c9-c14
Other Publication Details
Paper Reg. ID: IJNRD_184999
Published Paper Id: IJNRD2212202
Downloads: 000122256
Research Area: Engineering
Author Type: Indian Author
Country: Bangalore, karnataka, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2212202.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2212202
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