Artículo
Deep learning applied to intracranial hemorrhage detection
Autor/es | Cortés Ferre, Luis
Gutiérrez Naranjo, Miguel Ángel Egea Guerrero, Juan José Pérez Sánchez, Soledad Balcerzyk, Marcin |
Departamento | Universidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificial |
Fecha de publicación | 2023 |
Fecha de depósito | 2024-04-23 |
Publicado en |
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Resumen | 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. ... 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. Detection of, and diagnosis of, a hemorrhage that requires an urgent procedure 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 at a patient level and which could, 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 of hemorrhage or its lack of, and evaluates whether the patient is positive in terms of hemorrhage, and achieving, in this regard, 92.7% accuracy and 0.978 ROC AUC. On the other hand, our methodology provides visual explanations of the chosen classification using the Grad-CAM methodology. |
Cita | Cortés Ferre, L., Gutiérrez Naranjo, M.Á., Egea Guerrero, J.J., Pérez Sánchez, S. y Balcerzyk, M. (2023). Deep learning applied to intracranial hemorrhage detection. Journal of Imaging, 9 (2), 37. https://doi.org/10.3390/jimaging9020037. |
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