scholarly journals Performance of deep transfer learning for detecting abnormal fundus images

2020 ◽  
Vol 32 (4) ◽  
pp. 368
Author(s):  
ChangFan Wu ◽  
Yan Yu ◽  
Xiao Chen ◽  
XiangBing Zhu ◽  
PengFei Zhang ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Fundus Images

scholarly journals Literature Survey on Diabetic Retinopathy Classification Using Deep Learning

2021 ◽  
Vol 9 (8) ◽  
pp. 2259-2262
Author(s):  
Alfiya Md. Shaikh
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Transfer Learning ◽  
Medical Condition ◽  
Literature Survey ◽  
Fundus Images ◽  
Fundus Image ◽  
Automatic Diagnosis ◽  
Global Blindness

Abstract: Diabetic retinopathy (DR) is a medical condition that damages eye retinal tissues. Diabetic retinopathy leads to mild to complete blindness. It has been a leading cause of global blindness. The identification and categorization of DR take place through the segmentation of parts of the fundus image or the examination of the fundus image for the incidence of exudates, lesions, microaneurysms, and so on. This research aims to study and summarize various recent proposed techniques applied to automate the process of classification of diabetic retinopathy. In the current study, the researchers focused on the concept of classifying the DR fundus images based on their severity level. Emphasis is on studying papers that proposed models developed using transfer learning. Thus, it becomes vital to develop an automatic diagnosis system to support physicians in their work.

scholarly journals transferGWAS: GWAS of images using deep transfer learning

2021 ◽  
Author(s):  
Matthias Kirchler ◽  
Stefan Konigorski ◽  
Matthias Norden ◽  
Christian Meltendorf ◽  
Marius Kloft ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Medical Images ◽  
Genome Wide Association ◽  
Type I ◽  
Genome Wide Association Studies ◽  
Similar Data ◽  
Fundus Images ◽  
Genome Wide ◽  
Retinal Fundus Images ◽  
Retinal Fundus

Medical images can provide rich information about diseases and their biology. However, investigating their association with genetic variation requires non-standard methods. We propose transferGWAS, a novel approach to perform genome-wide association studies directly on full medical images. First, we learn semantically meaningful representations of the images based on a transfer learning task, during which a deep neural network is trained on independent but similar data. Then, we perform genetic association tests with these representations. We validate the type I error rates and power of transferGWAS in simulation studies of synthetic images. Then we apply transferGWAS in a genome-wide association study of retinal fundus images from the UK Biobank. This first-of-a-kind GWAS of full imaging data yielded 60 genomic regions associated with retinal fundus images, of which 7 are novel candidate loci for eye-related traits and diseases.

scholarly journals Detecting abnormal fundus images by employing deep transfer learning

2019 ◽  
Author(s):  
yan yu ◽  
Changfan Wu ◽  
Xiao Chen ◽  
Xiangbing Zhu ◽  
Yinfen Hou ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Learning Algorithm ◽  
Care Center ◽  
High Sensitivity ◽  
Vascular Lesion ◽  
Fundus Photography ◽  
Fundus Images ◽  
Validation Data ◽  
Data Set ◽  
Health Care Center

Abstract BackgroundTo develop and validate a deep transfer learning (DTL) algorithm in detecting abnormalities of fundus images from non-mydriatic fundus photography examination.Methods1,295 fundus images from January 2017 to December 2018 at Yijishan Hospital of Wannan Medical College were collected for developing and validating the deep transfer learning algorithm in detecting abnormal fundus images. The DTL model was developed by using 929(normal 254, abnormal 402) fundus images, including normal fundus images and abnormal fundus images, the latter including, maculopathy, optic neuropathy, vascular lesion, choroidal lesions, vitreous disease, cataract and the others. We tested our model using a subset of the publically available MESSIDOR dataset (using 366 images) and evaluate the testing performance of the DTL model for detecting abnormal fundus images. ResultsIn the internal validation data set (n=273 images), the AUC, sensitivity, accuracy and specificity of the DTL for correctly classified funds images were 0.997, 97.41%, 97.07% and 96.82%, respectively. For test data set (n=273 images), the AUC, sensitivity, accuracy and specificity of the DTL for correctly classification funds images were 0.926, 88.17%, 87.18% and 86.67%, respectively.ConclusionIn the evaluation, the DTL presented high sensitivity and specificity for detecting abnormal fundus-related diseases. Further research is necessary to improve this method and evaluate the applicability of the DTL in the community health care center.

The Diagnosis Of Diabetic Retinopathy By Means Of Transfer Learning And Fine-Tuned Dense Layer Pipeline

Mekatronika ◽  
2020 ◽  
Vol 2 (1) ◽  
pp. 68-72
Author(s):  
Abdulaziz Abdo Salman ◽  
Ismail Mohd Khairuddin ◽  
Anwar P.P. Abdul Majeed ◽  
Mohd Azraai Mohd Razman
Keyword(s):  
Blood Flow ◽  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Blood Vessels ◽  
Transfer Learning ◽  
The Body ◽  
Dense Layer ◽  
Fundus Images ◽  
Detection Approach ◽  
Deep Learning Model

Diabetes is a global disease that occurs when the body is disabled pancreas to secrete insulin to convert the sugar to power in the blood. As a result, some tiny blood vessels on the part of the body, such as the eyes, are affected by high sugar and cause blocking blood flow in the vessels, which is called diabetic retinopathy.  This disease may lead to permanent blindness due to the growth of new vessels in the back of the retina causing it to detach from the eyes. In 2016, 387 million people were diagnosed with Diabetic retinopathy, and the number is growing yearly, and the old detection approach becomes worse. Therefore, the purpose of this paper is to computerize the old method of detecting different classes of DR from 0-4 according to severity by given fundus images. The method is to construct a fine-tuned deep learning model based on transfer learning with dense layers. The used models here are InceptionV3, VGG16, and ResNet50 with a sharpening filter. Subsequently, InceptionV3 has achieved 94% as the highest accuracy among other models.  

scholarly journals Automatic glaucoma classification using color fundus images based on convolutional neural networks and transfer learning

2019 ◽  
Vol 10 (2) ◽  
pp. 892 ◽  
Author(s):  
Juan J. Gómez-Valverde ◽  
Alfonso Antón ◽  
Gianluca Fatti ◽  
Bart Liefers ◽  
Alejandra Herranz ◽  
...  
Keyword(s):  
Neural Networks ◽  
Transfer Learning ◽  
Convolutional Neural Networks ◽  
Fundus Images ◽  
Color Fundus Images
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