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The purpose of this study was to develop a deep learning-based method for the automated classification of renal cell carcinoma (RCC) from benign solid renal masses using contrast-enhanced computed tomography (CECT) images. The authors determined that a semi-automated majority voting convolutional neural network (CNN) based methodology enabled the accurate classification of RCC from benign neoplasms among solid renal masses on CECT.

Key points

  • Our proposed semi-automated majority voting CNN-based algorithm achieved accuracy of 83.75% for the diagnosis of RCC from benign solid renal masses on CECT images.
  • A fully automated CNN-based methodology classified solid renal masses with moderate accuracy of 77.36% using the same test images.
  • Employing 3D CNN-based methodology yielded slightly lower accuracy for renal mass classification compared with the semi-automated 2D CNN-based algorithm (79.24%).

Article: Automated classification of solid renal masses on contrast-enhanced computed tomography images using convolutional neural network with decision fusion

Authors: Fatemeh Zabihollahy, Nicola Schieda, Satheesh Krishna & Eranga Ukwatta

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This blog aims at bringing educational and critical perspectives on AI to readers. It should help imaging professionals to learn and keep up to date with the technologies being developed in this rapidly evolving field.