Erkennung Von Nierenkrebs Mit Hilfe Von Bildverarbeitungstechniken
Erkennung Von Nierenkrebs Mit Hilfe Von Bildverarbeitungstechniken

Erkennung Von Nierenkrebs Mit Hilfe Von Bildverarbeitungstechniken


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About the Book

Die Heilungsraten für Nierenkrebs variieren je nach Stadium und Grad; daher sind genaue Diagnoseverfahren für die Früherkennung und Diagnose von entscheidender Bedeutung. Einige Schwierigkeiten bei der manuellen Segmentierung haben den Einsatz von Deep-Learning-Modellen erforderlich gemacht, um Kliniker bei der effektiven Erkennung und Segmentierung von Krebs zu unterstützen. Probabilistische Faltungsneuronale Netze (Probabilistic Convolutional Neural Network, PCNN), insbesondere Faltungsneuronale Netze, haben hervorragende Erfolge bei der Klassifizierung und Segmentierung von Bildern erzielt. In diesem Projekt wird die Bildfilterung von MRI-Nierenbildern mit dem Algorithmus des bilateralen anisotropen Diffusionsfilters durchgeführt. Diese vorgeschlagene Vorverarbeitungstechnik bietet einen hohen Peak Signal to Noise Ratio (PSNR) und einen niedrigen Mean Square Error (MSE). Die Bildverbesserung von MRT-Nierenbildern wird mit dem Algorithmus Edge Preservation-Contrast Limited Adaptive Histogram Equalization (EP-CLAHE) durchgeführt. EP-CLAHE wird verwendet, um Kontrast und Helligkeit zu verbessern. Die Segmentierung von MRT-Nierenbildern wird mit dem Algorithmus Improved Fast Fuzzy C Means Clustering (IFFCMC) durchgeführt. IFFCMC wird verwendet, um die Nierenkrebs-Pixel zu segmentieren und andere Pixel auf dem MRT-Nierenbild zu unterdrücken.


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Product Details
  • ISBN-13: 9786208144876
  • Publisher: KS Omniscriptum Publishing
  • Binding: Paperback
  • Language: German
  • Returnable: N
  • Weight: 123 gr
  • ISBN-10: 6208144876
  • Publisher Date: 30 Sep 2024
  • Height: 229 mm
  • No of Pages: 76
  • Spine Width: 5 mm
  • Width: 152 mm


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Erkennung Von Nierenkrebs Mit Hilfe Von Bildverarbeitungstechniken
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Erkennung Von Nierenkrebs Mit Hilfe Von Bildverarbeitungstechniken
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