The effect of image resolution on convolutional neural networks in breast ultrasound.

ABSTRACT:

Purpose

The objective of this research was to investigate the efficacy of various parameter combinations of Convolutional Neural Networks (CNNs) models, namely MobileNet and DenseNet121, and different input image resolutions (REZs) ranging from 64×64 to 512×512 pixels, for diagnosing breast cancer.

Materials and methods

During the period of June 2015 to November 2020, two hospitals were involved in the collection of two-dimensional ultrasound breast images for this retrospective multicenter study. The diagnostic performance of the computer models MobileNet and DenseNet 121 was compared at different resolutions.

Results

The results showed that MobileNet had the best breast cancer diagnosis performance at 320×320pixel REZ and DenseNet121 had the best breast cancer diagn

SUBMITTER: Tang S 

PROVIDER: S-EPMC10469557 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature