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.; Pérez-Sánchez, S.; Balcerzyk, M. Deep Learning Applied to Intracranial Hemorrhage Detection. Preprints2021, 2021120150. https://doi.org/10.20944/preprints202112.0150.v2
Cortes-Ferre, L.; Gutiérrez-Naranjo, M. A.; Egea-Guerrero, J. J.; Pérez-Sánchez, S.; Balcerzyk, M. Deep Learning Applied to Intracranial Hemorrhage Detection. Preprints 2021, 2021120150. https://doi.org/10.20944/preprints202112.0150.v2
Cortes-Ferre, L.; Gutiérrez-Naranjo, M. A.; Egea-Guerrero, J. J.; Pérez-Sánchez, S.; Balcerzyk, M. Deep Learning Applied to Intracranial Hemorrhage Detection. Preprints2021, 2021120150. https://doi.org/10.20944/preprints202112.0150.v2
APA Style
Cortes-Ferre, L., Gutiérrez-Naranjo, M. A., Egea-Guerrero, J. J., Pérez-Sánchez, S., & Balcerzyk, M. (2022). Deep Learning Applied to Intracranial Hemorrhage Detection. Preprints. https://doi.org/10.20944/preprints202112.0150.v2
Chicago/Turabian Style
Cortes-Ferre, L., Soledad Pérez-Sánchez and Marcin Balcerzyk. 2022 "Deep Learning Applied to Intracranial Hemorrhage Detection" Preprints. https://doi.org/10.20944/preprints202112.0150.v2
Abstract
Intracranial hemorrhage is a serious medical problem that requires rapid and often intensive medical care. Identifying the location and type of any hemorrhage present is a critical step in the treatment of the patient. Diagnosis requires an urgent procedure, and the detection of hemorrhage is a difficult and time-consuming process for human experts. In this paper, we propose methods based on EfficientDet’s deep-learning technology that can be applied to the diagnosis of hemorrhages and thus become a decision-support system. Our proposal is two-fold. On the one hand, the proposed technique classifies slices of computed tomography scans for the presence hemorrhage or its lack, achieving 92.7% accuracy and 0.978 ROC-AUC. On the other hand, our methodology provides visual explanations of the classification chosen using the Grad-CAM methodology.
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.
Commenter: Marcin Balcerzyk
Commenter's Conflict of Interests: Author
Commenter:
Commenter's Conflict of Interests: I am an author