Preprint Article Version 2 Preserved in Portico This version is not peer-reviewed

Deep Learning Applied to Intracranial Hemorrhage Detection

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)

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 (doi: 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 (doi: 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

MATHEMATICS & COMPUTER SCIENCE, Artificial Intelligence & Robotics

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. 
+ Respond to this comment
Response 1 to Comment 1
Received: 4 February 2022
Commenter:
Commenter's Conflict of Interests: I am an author
Comment: This applies to the oficial version 2 of the preprint.

We encourage comments and feedback from a broad range of readers. See criteria for comments and our diversity statement.

Leave a public comment
Send a private comment to the author(s)
Views 0
Downloads 0
Comments 2
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.