DME-Deep: A Computerize Tool for Detection of Diabetic Macular Edema Grading Based on Multilayer Deep Learning and Transfer Learning

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dc.contributor.authorAbbas, Qaisaren_US
dc.date.accessioned2020-09-24T07:53:13Z
dc.date.available2020-09-24T07:53:13Z
dc.date.issued2020-06
dc.description.abstractDiabetic macular edema (DME) is a common disease of diabetic retinopathy (DR). Due to the infection of DME disease, many patients’ vision is lost. To cure DME eye disease, early detection and treatment are very important and vital steps. To automatically diagnosis DEM disease, several studies were developed by detection of the macula center which is dependent on optic disc (OD) location. In this paper, a novel features pre-training based model was proposed based on dense convolutional neural network (DCNN) to diagnose DME related disease. As a result, a computerize tool “DME-Deep” for detection of DME-based grading system was implemented through a new dense deep learning model and feature’s transfer learning approaches. This DCNN model was developed by adding new five convolutional and one dropout layers to the network. The DME-Deep system was tested on three different datasets, which obtained from online sources. To train the DCNN model for features learning, the 1650 retinal fundus images were utilized from the Hamilton HEI-MED, ISBI 2018 IDRiD and MESSIDOR datasets. On datasets, the DME-Deep achieved 91.2% of accuracy, 87.5% of sensitivity and 94.4% of specificity. Compare to obtain hand-crafted features, the automatic feature’ learning it provided favorable results. Hence, the experimental results also indicate that this DME-Deep system can automatically assist ophthalmologists in finding DEM eye-related disease.en_US
dc.identifier.affiliationsCollege of Computer and Information Sciences, AL Imam Muhammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabiaen_US
dc.identifier.citationAbbas Qaisar. DME-Deep: A Computerize Tool for Detection of Diabetic Macular Edema Grading Based on Multilayer Deep Learning and Transfer Learning. International Journal of Medical Research & Health Sciences. 2020 Jun; 9(6): 54-62en_US
dc.identifier.issn2319-5886
dc.identifier.placeIndiaen_US
dc.identifier.urihttps://imsear.searo.who.int/handle/123456789/205216
dc.languageenen_US
dc.publisherSumathi Publicationsen_US
dc.relation.issuenumber6en_US
dc.relation.volume9en_US
dc.source.urihttps://www.ijmrhs.com/abstract/dmedeep-a-computerize-tool-for-detection-of-diabetic-macular-edema-grading-based-on-multilayer-deep-learning-and-transfe-45248.htmlen_US
dc.subjectDiabetic retinopathyen_US
dc.subjectRetinal fundus imageen_US
dc.subjectDiabetic macular edemaen_US
dc.subjectDeep learningen_US
dc.subjectConvolutional neural networken_US
dc.subjectTransfer learningen_US
dc.titleDME-Deep: A Computerize Tool for Detection of Diabetic Macular Edema Grading Based on Multilayer Deep Learning and Transfer Learningen_US
dc.typeJournal Articleen_US
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