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ResNet + LSTM method for age-related macular degeneration and diabetic retinopathy diagnosis on OCT images
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Sun, Shichen. ResNet + LSTM method for age-related macular degeneration and diabetic retinopathy diagnosis on OCT images. Retrieved from https://doi.org/doi:10.7282/t3-42qf-9750

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TitleResNet + LSTM method for age-related macular degeneration and diabetic retinopathy diagnosis on OCT images
NameSun, Shichen (author); Metaxas, Dimitris N. (chair); Michmizos, Konstantinos (internal member); Melo, Gerard de (internal member); Rutgers University; School of Graduate Studies
Date Created2021
Other Date2021-01 (degree)
SubjectMachine learning, Optical coherence tomography, Computer Science
Extent1 online resource (vii, 16 pages) : illustrations
DescriptionWith the fast development of medical image devices and technologies, the amount of medical image data has increased significantly at a unprecedented rate. In many clinical specialities, there can be a shortage of doctors and radiologist experts to give timely diagnosis and referral to patients when facing the large amount of medical images and data. Especially for the ophthalmology, the widespread availability of optical coherence tomography (OCT), which is a novel medical imaging technology that allow acquisition of cross-sectional images of the retina with semihistologic resolution, has not been matched by the availability of human experts to interpret scans and provide appropriate treatments. According to the National Reporting and Learning System (NRLS), there are about 250 cases of sight harm due to the diagnosis delay per year in UK. Hence, how can we improve the efficiency of clinical diagnosis is a urgent problem need to be solved.

In this dissertation, we are going to present a method for classifying normal, diabetic retinopathy (DR) and age-related macular degeneration (AMD) on OCT scan sequences. Different from classic medical image classification problems, OCT is a crosssectional image sequences of eye fundus. The lesions of DR and AMD can be spatial related among OCT slices. To solve this problem and find the feature connection between OCT slices, we proposed a model that contains two neural networks. The first model is a residual neural network, which is used for extract symptom features on each OCT slices. The feature extract from each slices are combined to a series feature as the second model input. The second model is a long short-term memory neural network used for get the predictions based on OCT sequence feature. The model was trained and tested on around 1700 OCT scans, each of them has 25 slice images (Total 36149 slices). We used cross-entropy loss and k-cross validation. The result shows it has a accuracy of 1, 1, 1 on three class, respectively, when choosing the best model.
NoteM.S.
NoteIncludes bibliographical references
Genretheses, ETD graduate
Persistent URLhttps://doi.org/doi:10.7282/t3-42qf-9750
LanguageEnglish
CollectionSchool of Graduate Studies Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
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