0% Complete
Home
/
12th International Conference on Computer and Knowledge Engineering
Segmentation of Hard Exudates in Retinal Fundus Images Using BCDU-Net
Authors :
Nafise Ameri
1
Nasser Shoeibi
2
Mojtaba Abrishami
3
1- Ferdowsi university of mashhad
2- Eye Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
3- Eye Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
Keywords :
Fundus image،Diabetic retinopathy،Hard exudate،Deep learning،U-Net extended cannulation network،Lesion segmentation،Hard exudate segmentation
Abstract :
The importance of Diabetic Retinopathy (DR) screening and the difficulty in achieving an early diagnosis of DR at a reasonable cost requires attention to the development of computer-aided diagnostic tools. Computer-Aided Diagnosis (CAD) of retinal detachment imaging can reduce mass screening of the diabetic population and help physicians make the best use of their time. For this purpose, the deep learning technique and the developed U-Net canonization network have been used. Using this network, it receives retinal images and shows the segmentation of the hard exudate lesion as a binary image. The result of this research has been evaluated on the IDRID dataset with three important indicators of dice coefficient, sensitivity, and accuracy achieve at 76.81%, 72.24%, and 99.30%, respectively, and the effectiveness of the approach was confirmed.
Papers List
List of archived papers
SCDS: A Secure Clustering Protocol Using Dempster-Shafer Theory for VANET in Smart City
Hoda Mosadegh - Nazbanoo Farzaneh
FAHP-OF: A New Method for Load Balancing in RPL-based Internet of Things (IoT)
Mohammad Koosha - Behnam Farzaneh - Emad Alizadeh - Shahin Farzaneh
Improving LoRaWAN Scalability for IoT Applications using Context Information
Hamed Mahmoudi - Behrouz ShahgholiGhahfarokhi
Joint ADC-less Analog Demodulator and Decoder for Extended Binary (8, 4, 4) Hamming Channel Code
Mir Mahdi Safari - Jafar Pourrostam - Behzad Mozaffari Tazehkand
Cluster Sampling: A Cluster-Driven Sampling Strategy for Deep Metric Learning
Hamideh Rafiee - Ahmad Ali Abin - Seyed Soroush Majd
A large input-space-margin approach for adversarial training
Reihaneh Nikouei - Mohammad Taheri
InfOnto: An ontology for fashion influencer marketing based on Instagram
Somaye Sultani - Mohsen Kahani
An Energy-efficient Clustering Method based on Butterfly Optimization Algorithm by Considering the Criterion of Intra-cluster Distances in WSNs
Fariba Saghi Hadi S. Aghdasi
Optimizing Question-Answering Framework Through Integration of Text Summarization Model and Third-Generation Generative Pre-Trained Transformer
Ervin Gubin Moung - Toh Sin Tong - Maisarah Mohd Sufian - Valentino Liaw - Ali Farzamnia - Farashazillah Yahya
EEMC: Energy Efficient Multi-Clustering Using Grey Wolf Optimizer in WSNs
Maryam Ghorbanvirdi - Sayyed Majid Mazinani
more
Samin Hamayesh - Version 41.7.6