Deep Learning Applied to Intracranial Hemorrhage Detection

Luis Cortes-Ferre; Miguel Angel Gutiérrez-Naranjo; Juan José Egea-Guerrero; Soledad Pérez-Sánchez; Marcin Balcerzyk

doi:10.20944/preprints202112.0150.v2

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preprints.org > computer science and mathematics > artificial intelligence and machine learning > doi: 10.20944/preprints202112.0150.v2

Preprint Article Version 2 Preserved in Portico This version is not peer-reviewed

Deep Learning Applied to Intracranial Hemorrhage Detection

Luis Cortes-Ferre

Miguel Angel Gutiérrez-Naranjo

Juan José Egea-Guerrero

Soledad Pérez-Sánchez

Marcin Balcerzyk

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. 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. Preprints 2021, 2021120150. https://doi.org/10.20944/preprints202112.0150.v2

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MDPI and ACS Style

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

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.

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Comments (2)

Comment 1

Received: 3 February 2022
Commenter: Marcin Balcerzyk
Commenter's Conflict of Interests: Author

Comment: Version 3 had authors updated on the web page, which now corresponds to the PDF. Abstract is also updated to the PDF version.

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Response 1 to Comment 1

Received: 4 February 2022
Commenter:

Marcin Balcerzyk

Commenter's Conflict of Interests: I am an author

Comment: This applies to the oficial version 2 of the preprint.

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