Version 1
: Received: 7 December 2021 / Approved: 9 December 2021 / Online: 9 December 2021 (10:49:02 CET)
Version 2
: Received: 1 February 2022 / Approved: 3 February 2022 / Online: 3 February 2022 (12:10:05 CET)
Version 3
: Received: 19 December 2022 / Approved: 20 December 2022 / Online: 20 December 2022 (10:31:23 CET)
How to cite:
Cortes-Ferre, L.; Gutiérrez-Naranjo, M.A.; Egea-Guerrero, J.J.; Balcerzyk, M. Deep Learning Applied to Intracranial Hemorrhage Detection. Preprints2021, 2021120150. https://doi.org/10.20944/preprints202112.0150.v1
Cortes-Ferre, L.; Gutiérrez-Naranjo, M.A.; Egea-Guerrero, J.J.; Balcerzyk, M. Deep Learning Applied to Intracranial Hemorrhage Detection. Preprints 2021, 2021120150. https://doi.org/10.20944/preprints202112.0150.v1
Cortes-Ferre, L.; Gutiérrez-Naranjo, M.A.; Egea-Guerrero, J.J.; Balcerzyk, M. Deep Learning Applied to Intracranial Hemorrhage Detection. Preprints2021, 2021120150. https://doi.org/10.20944/preprints202112.0150.v1
APA Style
Cortes-Ferre, L., Gutiérrez-Naranjo, M.A., Egea-Guerrero, J.J., & Balcerzyk, M. (2021). Deep Learning Applied to Intracranial Hemorrhage Detection. Preprints. https://doi.org/10.20944/preprints202112.0150.v1
Chicago/Turabian Style
Cortes-Ferre, L., Juan José Egea-Guerrero and Marcin Balcerzyk. 2021 "Deep Learning Applied to Intracranial Hemorrhage Detection" Preprints. https://doi.org/10.20944/preprints202112.0150.v1
Abstract
Intracranial hemorrhage is a serious health problem requiring rapid and often intensive medical care. Identifying the location and type of any hemorrhage present is a critical step in treating the patient. Diagnosis requires an urgent procedure and the detection of the hemorrhage is a hard and time-consuming process for human experts. In this paper, we propose a novel method based on Deep Learning techniques which can be useful as decision support system. Our proposal is two-folded. On the one hand, the proposed technique classifies slices of computed tomography scans for hemorrhage existence or not, achieving 92.7% accuracy and 0.978 ROC-AUC. On the other hand, our method provides visual explanation to the chosen classification by using the so-called Grad-CAM method.
Keywords
Image Detection; Intracranial Hemorrhage; Deep Learning; Decision Support System
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
