Retinal image quality assessment using transfer learning: Spatial images vs. wavelet detail subbands
Retinal image quality assessment (RIQA) is essential to assure that images used for medical analysis are of sufficient quality for reliable diagnosis. A modified VGG16 network with transfer learning is introduced in order to classify retinal images into good or bad quality images. Both spatial and w...
Main Author: | Lamiaa Abdel-Hamid |
---|---|
Format: | Article |
Language: | English |
Published: |
Elsevier
2021-09-01
|
Series: | Ain Shams Engineering Journal |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S2090447921001015 |
Similar Items
-
VGG16 Transfer Learning Architecture for Salak Fruit Quality Classification
by: Rismiyati Rismiyati, et al.
Published: (2021-03-01) -
COV-VGX: An automated COVID-19 detection system using X-ray images and transfer learning
by: Prottoy Saha, et al.
Published: (2021-01-01) -
Deep convolutional neural networks for surface coal mines determination from sentinel-2 images
by: L. Madhuanand, et al.
Published: (2021-01-01) -
Visual Cross-Image Fusion Using Deep Neural Networks for Image Edge Detection
by: Zhong Qu, et al.
Published: (2019-01-01) -
Wavelet-Based Enhanced Medical Image Super Resolution
by: Farah Deeba, et al.
Published: (2020-01-01)