Optical coherence tomography image for automatic classification of diabetic macular edema

• Autorzy: Wang Ping , Li Jia-Li , Ding Hao

Identyfikatory

DOI

10.37190/oa200405

• Języki publikacji: EN

Abstrakty

EN

Diabetic macular edema (DME) is the dominant reason of diabetic visual loss, so early detection and treatment of DME is of great significance for the treatment of diabetes. Based on transfer learning, an automatic classification method is proposed to distinguish DME images from normal images in optical coherence tomography (OCT) retinal fundus images. Features of the DME are automatically identified and extracted by the pre-trained convolutional neural network (CNN), which only involves fine-tuning the VGGNet-16 network without any user intervention. An accuracy of 97.9% and a sensitivity of 98.0% are acquired with the OCT images in the Duke data set from experimental results. The proposed method, a core part of an automated diagnosis system of the DME, revealed the ability of fine-tuning models to train non-medical images, allowing them can be classified with limited training data. Moreover, it can be developed to assist early diagnosis of the disease, effectively delaying (or avoiding) the progression of the disease, consequently.

Słowa kluczowe

EN

diabetic macular edema; optical coherence tomography; transfer learning; fine-tuning

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: Optica Applicata
• Rocznik: 2020
• Tom: Vol. 50, nr 4
• Strony: 567--577
• Opis fizyczny: Bibliogr. 28 poz., rys., tab.

Twórcy

autor

Wang Ping

• Department of Electronics Information Engineering, School of Information Engineering, Nanchang University, Nanchang 330031, China

autor

Li Jia-Li

• Department of Electronics Information Engineering, School of Information Engineering, Nanchang University, Nanchang 330031, China

autor

Ding Hao

• Department of Electronics Information Engineering, School of Information Engineering, Nanchang University, Nanchang 330031, China

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).