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
Impact of deep learning reconstruction on radiation dose reduction and cancer risk in CT examinations
Deep‑learning reconstruction (DLR) shifts CT image formation from a hardware‑limited process to a data‑driven one. In our real‑world cohort of >10,000 body scans, we observed a
28. April 2025
