Fundus imaging is a retinal image modality for capturing anatomical structures and
abnormalities in the human eye. Fundus images are the primary tool for observation and …

Crop discrimination at the plant or patch level is vital for modern technology-enabled
agriculture. Multispectral and hyperspectral remote sensing data have been widely used for …

Image classification using convolutional neural networks (CNNs) outperforms other state-of-
the-art methods. Moreover, attention can be visualized as a heatmap to improve the …

There are many kinds of fundus diseases, and early diagnosis is the key to prevent severe
visual impairment. In this paper, we propose a deep learning model to automatically detect …

The main objective of this paper is to propose transfer learning technique for multiclass
multilabel opthalmological diseases prediction in fundus images by using the one versus …

Various ocular diseases such as cataracts, glaucoma, and diabetic retinopathy have
become several major factors causing non-congenital visual impairment, which seriously …

Recently, automated retinal image processing has been considered a competitive field of
research due to the low-accuracy results, complexity, and unacceptable outcomes …

Aiming at the problem that there may be one or more diseases and unbalanced distribution
of labels in fundus images, in this paper proposes a multi-label classification method for …

Deep Learning-based medical imaging research has been actively conducted thanks to its
high diagnostic accuracy comparable to that of expert physicians. However, to apply …

Purpose: Retinopathy screening via digital imaging is promising for early detection and
timely treatment, and tracking retinopathic abnormality over time can help to reveal the risk …