Retinal Vessels Detection Using Convolutional Neural Networks in Fundus Images
Islam, M. M.; Poly, T. N.; Li, Y.-C.
Show abstract
Computer-aided detection (CAD) system is a realistic option for physicians to screen fundus images. Automated segmentation of retinal vessel is in fundus important step to identify the retinal disease region. However, identification of the retinal disease region accurately is still challenging due to the varied distribution of blood vessel on noisy and low contrast fundus images. Healthcare system has been changing significantly with the emergence of machine learning (ML), deep learning (DL) and artificial intelligence (AI) in recent year. Retinal vessel detection is one such area of application of deep learning, for improving the accuracy of detection and segmentation and the quality of patient care. Recently, the convolutional neural networks (CNN) have been applied to the detection of the retinal vessel from fundus images and have demonstrated promising results. The range of accuracy of the CNN model was 0.91-0.95 and the area under the receiver operating curve was 0.09-0.98. Therefore, CNN may play a crucial role in determining the therapeutic methods and detecting the retinal vessel accurately in an individual manner. In this survey, we described the use of CNN in fundus imaging, especially focused on CNN technique, clinical application for retinal vessel detection and future prospective.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- An Inexpensive Smartphone-Based Device and Predictive Models for Rapid, Non-Invasive, and Point-of-Care Monitoring of Ocular and Cardiovascular Complications Related to Diabetes 95%
- Extensive In Silico Analysis of the Functional and Structural Consequences of SNPs in Human ARX Gene 94%
- DermoExpert: Skin lesion classification using a hybrid convolutional neural network through segmentation, transfer learning, and augmentation 93%
Similar papers in this journal
- Estimation of Three-Dimensional Chromatin Morphology for Nuclear Classification and Characterisation 95%
- A Convolution Based Computational Approach Towards DNA N6-methyladenine Site Identification and Motif Extraction in Rice Genome 95%
- Classification models for Invasive Ductal Carcinoma Progression, based on gene expression data-trained supervised machine learning 95%
Similar papers in this journal
- Investigate the relevance of major signaling pathways in cancer survival using a biologically meaningful deep learning model 94%
- Rprot-Vec: A deep learning approach for fast protein structure similarity calculation 93%
- RAFTS3G - An efficient and versatile clustering software to analyses in large protein datasets 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.