Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed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)

How to cite: Cortes-Ferre, L.; Gutiérrez-Naranjo, M.A.; Egea-Guerrero, J.J.; Balcerzyk, M. Deep Learning Applied to Intracranial Hemorrhage Detection. Preprints 2021, 2021120150 (doi: 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 (doi: 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

MATHEMATICS & COMPUTER SCIENCE, Artificial Intelligence & Robotics

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