Effect of Input Size on the Classification of Lung Nodules Using Convolutional Neural Networks

Autor: Gorkem Polat, Ugur Halici, Yesim Serinagaoglu
Rok vydání: 2021
Předmět:
FOS: Computer and information sciences
Computer Science - Machine Learning
Computer science
Radiography
Computer Vision and Pattern Recognition (cs.CV)
0206 medical engineering
Computer Science - Computer Vision and Pattern Recognition
CAD
02 engineering and technology
Convolutional neural network
030218 nuclear medicine & medical imaging
Machine Learning (cs.LG)
03 medical and health sciences
0302 clinical medicine
medicine
False positive paradox
FOS: Electrical engineering
electronic engineering
information engineering
Sensitivity (control systems)
Lung cancer
business.industry
Image and Video Processing (eess.IV)
Pattern recognition
Electrical Engineering and Systems Science - Image and Video Processing
medicine.disease
020601 biomedical engineering
Artificial intelligence
business
Lung cancer screening
Volume (compression)
DOI: 10.48550/arxiv.2107.05085
Popis: Recent studies have shown that lung cancer screening using annual low-dose computed tomography (CT) reduces lung cancer mortality by 20% compared to traditional chest radiography. Therefore, CT lung screening has started to be used widely all across the world. However, analyzing these images is a serious burden for radiologists. The number of slices in a CT scan can be up to 600. Therefore, computer-aided-detection (CAD) systems are very important for faster and more accurate assessment of the data. In this study, we proposed a framework that analyzes CT lung screenings using convolutional neural networks (CNNs) to reduce false positives. We trained our model with different volume sizes and showed that volume size plays a critical role in the performance of the system. We also used different fusions in order to show their power and effect on the overall accuracy. 3D CNNs were preferred over 2D CNNs because 2D convolutional operations applied to 3D data could result in information loss. The proposed framework has been tested on the dataset provided by the LUNA16 Challenge and resulted in a sensitivity of 0.831 at 1 false positive per scan.
Comment: 4 pages, in Turkish language, 2018 26th Signal Processing and Communications Applications Conference (SIU)
Databáze: OpenAIRE
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