An Efficient Transfer Learning Based Approach for Detecting the Abnormal Fundus Images

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.

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transferGWAS: GWAS of images using deep transfer learning

10.1101/2021.10.22.465430 ◽

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.

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Comparative Study of Transfer Learning Models for Retinal Disease Diagnosis from Fundus Images

Computers Materials & Continua ◽

10.32604/cmc.2022.021943 ◽

2022 ◽

Vol 70 (3) ◽

pp. 5821-5834

Author(s):

Kuntha Pin ◽

Jee Ho Chang ◽

Yunyoung Nam

Keyword(s):

Comparative Study ◽

Transfer Learning ◽

Retinal Disease ◽

Disease Diagnosis ◽

Fundus Images ◽

Learning Models

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Detecting abnormal fundus images by employing deep transfer learning

10.21203/rs.2.13807/v1 ◽

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.

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