Deployment of CNN on colour fundus images for the automatic detection of glaucoma

Abstract

Detection of glaucoma has become critical, as it has arisen as the subsequent essential driver of visual impairment, around the world. At present, most of the algorithms in use rely on pre-trained deep neural networks to produce the best results. However, the high computational time and complexity and the need of a large database, make glaucomadetection arduous and difficult. Keeping these in mind, this paper proposes a new convolutional neural network architecture, in particular, ProspectNet, which has demonstrated to accomplish a better accuracy with lesser computational time and complexity when tested against two pre-trained networks: VGG16 and DenseNet121. The data set is an amalgamation of two publicly available datasets-DRISHTI-GS and Glaucoma Dataset (Kaggle), comprising ocular colour fundus images of glaucomatous as well as normal eyes. ProspectNet has accomplished a normal AUC (area under the curve) as 0.991, specificity, and precision as 0.98. Confusion matrices also plotted to illustrate the new architecture's efficacy. These outcomes demonstrate that ProspectNet is a hearty option in contrast to other best in class calculations for a medium sized dataset. The paper suggests three distinct structures for glaucoma detection. One advantage of our approach is that no special feature selection, such as detailed measurements of particular traits like the structure of the optic nerve head, is necessary.

Identification of Peruvian timber species based on image processing and convolutional neural networks

“…In the 3 models, the last layer is changed to a softmax layer with 11 outputs for each of the timber species. MobileNet and VGG-16 are pre-trained in the ImageNet dataset, consisting of around 14 million images with more than 20000 classes (Ghorui et al, 2023). Training lasts for 20 epochs with a batch size of 32, using the Adam (Kingma and Ba, 2014) optimizer with a learning rate of 0.001.…”

Automated Glaucoma Detection Techniques: an Article Review

“…To differentiate healthy from non-healthy images, CNN reached an accuracy of 99.67% on databases of Diabetic Retinopathy (DR) and 96.5% on databases of Glaucoma (GL). Furthermore, in the context of non-healthy screening, aiming to differentiate between different retinal disorders, CNN achieved an accuracy of 99.03% when distinguishing between cases of GL and DR. Ghorui A. et al,[27] proposed a novel CNN architecture called ProspectNet. It outperforms two established pre-trained networks, VGG16 and DenseNet121, by exhibiting higher accuracy with reduced computational time and complexity.…”