Anzeige
Erschienen in:
2024 | OriginalPaper | Buchkapitel
Hemorrhage Detection from Whole-Body CT Images Using Deep Learning
verfasst von : Anandakumar Haldorai, Babitha Lincy R, Suriya Murugan, Minu Balakrishnan
Erschienen in: Artificial Intelligence for Sustainable Development
Verlag: Springer Nature Switzerland
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Abstract
In medical applications, deep learning has shown to be a powerful tool, especially when it comes to identifying patterns in healthcare datasets. Radiologists’ evaluation of CT images is crucial to the prompt identification of cerebral bleeding. The dataset used in this investigation included 3000 patients’ full-body DICOM CT scans. After segmenting these scans to separate the brain pictures, clustering was used to put them in groups according to visual similarity. This method increases the possibility of reliably and effectively identifying cerebral hemorrhage, which may have an effect on patient outcomes. Further convolutional neural network (CNN) is applied to find patterns in Brain CT scans of patients to correctly detect internal bleeding and classify hemorrhage and nonhemorrhage images.