An Improved Deep Learning Framework for Automated Optic Disc Localization and Glaucoma Detection  

作　　者：Hela Elmannai Monia Hamdi Souham Meshoul Amel Ali Alhussan Manel Ayadi Amel Ksibi 

机构地区：[1]Department of Information Technology,College of Computer and Information Sciences,Princess Nourah bint Abdulrahman University,P.O.Box 84428,Riyadh,11671,Saudi Arabia [2]Department of Computer Sciences,College of Computer and Information Sciences,Princess Nourah bint Abdulrahman University,P.O.Box 84428,Riyadh,11671,Saudi Arabia [3]Department of Information System,College of Computer and Information Sciences,Princess Nourah bint Abdulrahman University,P.O.Box 84428,Riyadh,11671,Saudi Arabia

出　　处：《Computer Modeling in Engineering & Sciences》2024年第8期1429-1457,共29页工程与科学中的计算机建模（英文）

基　　金：Deanship of Scientific Research,Princess Nourah bint Abdulrahman University,through the Program of Research Project Funding after Publication,Grant No(43-PRFA-P-31).

摘　　要：Glaucoma disease causes irreversible damage to the optical nerve and it has the potential to cause permanent loss of vision.Glaucoma ranks as the second most prevalent cause of permanent blindness.Traditional glaucoma diagnosis requires a highly experienced specialist,costly equipment,and a lengthy wait time.For automatic glaucoma detection,state-of-the-art glaucoma detection methods include a segmentation-based method to calculate the cup-to-disc ratio.Other methods include multi-label segmentation networks and learning-based methods and rely on hand-crafted features.Localizing the optic disc(OD)is one of the key features in retinal images for detecting retinal diseases,especially for glaucoma disease detection.The approach presented in this study is based on deep classifiers for OD segmentation and glaucoma detection.First,the optic disc detection process is based on object detection using a Mask Region-Based Convolutional Neural Network(Mask-RCNN).The OD detection task was validated using the Dice score,intersection over union,and accuracy metrics.The OD region is then fed into the second stage for glaucoma detection.Therefore,considering only the OD area for glaucoma detection will reduce the number of classification artifacts by limiting the assessment to the optic disc area.For this task,VGG-16(Visual Geometry Group),Resnet-18(Residual Network),and Inception-v3 were pre-trained and fine-tuned.We also used the Support Vector Machine Classifier.The feature-based method uses region content features obtained by Histogram of Oriented Gradients(HOG)and Gabor Filters.The final decision is based on weighted fusion.A comparison of the obtained results from all classification approaches is provided.Classification metrics including accuracy and ROC curve are compared for each classification method.The novelty of this research project is the integration of automatic OD detection and glaucoma diagnosis in a global method.Moreover,the fusion-based decision system uses the glaucoma detection result obtained using several

关 键 词：Optic disc GLAUCOMA fundus image deep learning 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]