What is it about?
Microaneurysm (MA) is the earliest lesion of diabetic retionpathy. This paper proposes a deconvolutional neural network to accurately discriminate MA from non-MA. Experimental results demonstrate that, the proposed method is able to achieve significant sensitivity and accuracy on multiple public datasets, in comparison to the state-of-the-art.
Photo by v2osk on Unsplash
Why is it important?
An efficient network is proposed to accurately detect MA in retinal funds image. Compared to the traditional CNN using a large number of training samples, the random under-sampling based SODNet can detect small object with imbalanced data, in terms of few training samples.
Read the Original
This page is a summary of: SODNet: Small Object Detection Using Deconvolutional Neural Network, IET Image Processing, April 2020, the Institution of Engineering and Technology (the IET), DOI: 10.1049/iet-ipr.2019.0833.
You can read the full text:
The following have contributed to this page