Ensembled Deep Neural Network for Intracranial Hemorrhage Detection and Subtype Classification on Noncontrast CT Images

Authors

Yunan Wu^{1, 2,} , Mark P. Supanich^1, , Jie Deng^{1, *}

¹Department of Diagnostic Radiology, Rush University Medical Center, 1653 W. Congress Pkwy, Jelke Street 181, Chicago, IL, 60612, USA

²Department of Electrical Computer Engineering, Northwestern University, 633 Clark Street, Evanston, IL, 60208, USA

^*Corresponding author. Email: Jie_Deng@rush.edu

DOI

10.2991/jaims.d.210618.001How to use a DOI?

Keywords

Intracranial hemorrhage; Subtype classification; Computer tomography; Deep learning; Ensembled model

Abstract

Rapid and accurate diagnosis of intracranial hemorrhage is clinically significant to ensure timely treatment. In this study, we developed an ensembled deep neural network for the detection and subtype classification of intracranial hemorrhage. The model consisted of two parallel network pathways, one using three different window level/width settings to enhance the image contrast of brain, blood, and soft tissue. The other extracted spatial information of adjacent image slices to the target slice. Both pathways exploited the EfficientNet-B0 as the basic architecture and were ensembled to generate the final prediction. Class activation mapping was applied in both pathways to highlight the regions of detected hemorrhage and the associated subtypes. The model was trained and tested using Intracranial Hemorrhage Detection Challenge (IHDC) dataset launched by the Radiological Society of North America (RSNA) in 2019, which contained 674,258 head noncontrasts computer tomography images acquired from 19,530 patients. An independent dataset (CQ500) acquired from another institution was used to test the generalizability of the trained model. The overall accuracy, sensitivity, and F1 score for intracranial hemorrhage detection were 95.7%, 85.9%, and 86.7% on IHDC testing dataset and 92.4%, 92.6%, and 93.4% on external CQ500 testing dataset. The heatmaps by class activation mapping successfully demonstrated discriminative feature regions of the predicted hemorrhage locations and subtypes, providing visual guidance for radiologists to assist in rapid diagnosis of intracranial hemorrhage.

Copyright

© 2021 The Authors. Published by Atlantis Press B.V.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Yunan Wu
AU  - Mark P. Supanich
AU  - Jie Deng
PY  - 2021
DA  - 2021/06/23
TI  - Ensembled Deep Neural Network for Intracranial Hemorrhage Detection and Subtype Classification on Noncontrast CT Images
JO  - Journal of Artificial Intelligence for Medical Sciences
SP  - 12
EP  - 20
VL  - 2
IS  - 1-2
SN  - 2666-1470
UR  - https://doi.org/10.2991/jaims.d.210618.001
DO  - 10.2991/jaims.d.210618.001
ID  - Wu2021
ER  -