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.
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. TRANSLATE with x English ArabicHebrewPolish BulgarianHindiPortuguese CatalanHmong DawRomanian Chinese SimplifiedHungarianRussian Chinese TraditionalIndonesianSlovak CzechItalianSlovenian DanishJapaneseSpanish DutchKlingonSwedish EnglishKoreanThai EstonianLatvianTurkish FinnishLithuanianUkrainian FrenchMalayUrdu GermanMalteseVietnamese GreekNorwegianWelsh Haitian CreolePersian TRANSLATE with COPY THE URL BELOW Back EMBED THE SNIPPET BELOW IN YOUR SITE Enable collaborative features and customize widget: Bing Webmaster Portal Back TRANSLATE with x English ArabicHebrewPolish BulgarianHindiPortuguese CatalanHmong DawRomanian Chinese SimplifiedHungarianRussian Chinese TraditionalIndonesianSlovak CzechItalianSlovenian DanishJapaneseSpanish DutchKlingonSwedish EnglishKoreanThai EstonianLatvianTurkish FinnishLithuanianUkrainian FrenchMalayUrdu GermanMalteseVietnamese GreekNorwegianWelsh Haitian CreolePersian TRANSLATE with COPY THE URL BELOW Back EMBED THE SNIPPET BELOW IN YOUR SITE Enable collaborative features and customize widget: Bing Webmaster Portal Back
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.
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