Pregled bibliografske jedinice broj: 1218530
Annotated Lung CT Image Database
Annotated Lung CT Image Database // Proceedings of ELMAR-2022 / Mustra, Mario ; Zovko-Cihlar, Branka ; Vukovic, Josip (ur.).
Zadar, Hrvatska: Institute of Electrical and Electronics Engineers (IEEE), 2022. str. 165-168 doi:10.1109/elmar55880.2022.9899805 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
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Naslov
Annotated Lung CT Image Database
Autori
Ivusic, David ; Petrak, Antun ; Bozek, Jelena ; Grgic, Sonja
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
Proceedings of ELMAR-2022
/ Mustra, Mario ; Zovko-Cihlar, Branka ; Vukovic, Josip - : Institute of Electrical and Electronics Engineers (IEEE), 2022, 165-168
ISBN
978-1-6654-7004-9
Skup
64th International Symposium ELMAR-2022
Mjesto i datum
Zadar, Hrvatska, 12.09.2022. - 14.09.2022
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
Lung ; CT ; Database ; Region of Interest ; Neural Network ; YOLO algorithm ; ALCTID
Sažetak
Computed tomography (CT) of lungs provides a diagnostic tool for identifying a range of lesions and diseases visible in the obtained scans. For helping radiologists in a timely and efficient assessment of a large number of scans different machine learning methods have been applied for the detection and classification of abnormalities. However, before clinical usage of such algorithms it is necessary to achieve high accuracy of the algorithm. This is achieved through training and testing phases for which it is essential to have a database that would include a range of abnormalities in the lungs. Here we propose a novel database ALCTID (Annotated Lung CT Image Database) with regions of interest (ROI) annotated by an experienced thoracic radiologist. Database includes 170 lung CT images with a total of 307 annotated ROIs comprising a range of abnormalities, from cancerous lesions, enlarged lymph nodes to enlarged heart and edema. To demonstrate the applicability of the novel database we trained and tested convolution neural network based on the YOLO (You Only Look Once) algorithm on 170 images with annotated ROIs and 170 images of healthy lungs. Out of 80 annotated ROIs in the test set, the network correctly detected 56 ROIs, with 12 false positives and 25 false negatives. The new database ALCTID is publicly available at http://www.vcl.fer.hr/alctid.
Izvorni jezik
Engleski
Znanstvena područja
Elektrotehnika, Računarstvo
POVEZANOST RADA
Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb
