Investigation of Transfer Learning on Pulmonary Nodule Characteristics

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Studies on the classification of small pulmonary nodules generally focus on the prediction of malignancy of the nodule. In the recent years, publicly available databases provided different types of data to researchers, such as nodule characteristics, apart from the lung image and malignancy degree. In this paper, a study on the classification of pulmonary nodule characteristics using conventional features and deep features obtained from transfer learning method has been proposed. The results were assessed by sensitivity, specificity, and classification accuracy. The results of the study can be used to form multi-level classifiers in predicting malignancy by combining different types of features.
Original languageTurkish
Title of host publication2017 25th Signal Processing And Communications Applications Conference (siu)
PublisherIEEE Canada
Number of pages4
ISBN (Electronic)978-1-5090-6494-6
Publication statusPublished - 2017
Event25th Signal Processing and Communications Applications Conference (SIU) - Antalya, Turkey
Duration: 15 May 201718 May 2017

Publication series

NameSignal Processing And Communications Applications Conference

Conference

Conference25th Signal Processing and Communications Applications Conference (SIU)
Country/TerritoryTurkey
CityAntalya
Period15/05/1718/05/17

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