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Deep Learning for Renal Cell Carcinoma Detection by Alim Sumon is a document available to read on EtoBox.

What is Deep Learning for Renal Cell Carcinoma Detection about?

This study presents a deep learning algorithm for detecting and subtyping renal cell carcinoma (RCC) using a 2D neural network architecture with feature consistency techniques. The FocalNet-DINO model achieved a recall rate of 0.823 at 0.025 false positives per image, demonstrating improved accuracy and sensitivity in RCC detection and classification. The integration of spatial and class consistency modules further enhanced the model

Author

Alim Sumon

Language

EN