2020
DOI: 10.1007/s12652-020-02460-7
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RETRACTED ARTICLE: Deep CNN framework for retinal disease diagnosis using optical coherence tomography images

Abstract: Accurate and robust diagnosis of retinal diseases using OCT imaging is considered an essential part for clinical utility. We propose a deep learning based, a fully automated diagnosis system for detecting retinal disorders namely, Drusen macular degeneration (DMD) and diabetic macular edema (DME) using optical coherence tomography (OCT) Images. If it is not diagnosed and treated, these degenerative abnormalities may result in moderate to severe vision loss. Early detection of these diseases reduces the risk of… Show more

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Cited by 43 publications
(18 citation statements)
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“…We addressed this issue by using transfer learning, as well as data augmentation techniques that were extensively used in previous studies including medical applications. 83 90 Furthermore, lack of a public OCT dataset that includes the Pelli-Robson contrast sensitivity information made this problem of testing the model on other datasets challenging. Also, as shown in previous studies, 91 93 we cannot rule out the fact that various factors such as individual differences in axial length, gender, retinal disease, or age might have affected the accuracy of OCT retinal thickness measurements.…”
Section: Discussionmentioning
confidence: 99%
“…We addressed this issue by using transfer learning, as well as data augmentation techniques that were extensively used in previous studies including medical applications. 83 90 Furthermore, lack of a public OCT dataset that includes the Pelli-Robson contrast sensitivity information made this problem of testing the model on other datasets challenging. Also, as shown in previous studies, 91 93 we cannot rule out the fact that various factors such as individual differences in axial length, gender, retinal disease, or age might have affected the accuracy of OCT retinal thickness measurements.…”
Section: Discussionmentioning
confidence: 99%
“…The method was verified on 2 datasets, involving real person retinal fundus images attained from nearby hospitals. Rajagopalan et al [12] proposed a DCNN architecture for the classification and diagnosis of Average, DME, and DMD efficiently. Firstly, despeckling of the input OCT image is executed by the Kuan filters for removing inherent speckle noise.…”
Section: Related Workmentioning
confidence: 99%
“…The accuracy was 87.5%, with a sensitivity and a specificity of 93.5% and 81%, respectively. Another algorithm has been introduced by Rajagopalan et al 12 The CNN framework was used to create the algorithm, which was then trained using the K‐fold validation technique. Taking this into consideration, the dataset was preprocessed using the Kuan filter to remove the speckle noise.…”
Section: Related Workmentioning
confidence: 99%