Convolutional neural networks for the differentiation between benign and malignant renal tumors with a multicenter international computed tomography dataset.

ABSTRACT:

Objectives

To use convolutional neural networks (CNNs) for the differentiation between benign and malignant renal tumors using contrast-enhanced CT images of a multi-institutional, multi-vendor, and multicenter CT dataset.

Methods

A total of 264 histologically confirmed renal tumors were included, from US and Swedish centers. Images were augmented and divided randomly 70%:30% for algorithm training and testing. Three CNNs (InceptionV3, Inception-ResNetV2, VGG-16) were pretrained with transfer learning and fine-tuned with our dataset to distinguish between malignant and benign tumors. The ensemble consensus decision of the three networks was also recorded. Performance of each network was assessed with receiver operating characteristics (ROC) curves and their area under the cu

SUBMITTER: Klontzas ME 

PROVIDER: S-EPMC10811309 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature