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

Research Articie June 24,2021

Yunan Wu ¹ , Mark P. Supanich ² , Jie Deng ³ hide author's information

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

DOI: https://doi.org/10.2991/jaims.d.210618.001

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

Cite this article: Wu Y, Supanich MP, Jie D. Ensembled Deep Neural Network for Intracranial Hemorrhage Detection and Subtype Classification on Noncontrast CT Images. JAIMS [Internet]. 2021; 2(1-2):12-20

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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....