Detection of Diabetic Retinopathy Using Bichannel Convolutional Neural Network

Abstract

Deep learning of fundus photograph has emerged as a practical and cost-effective technique for automatic screening and diagnosis of severer diabetic retinopathy (DR). The entropy image of luminance of fundus photograph has been demonstrated to increase the detection performance for referable DR using a convolutional neural network- (CNN-) based system. In this paper, the entropy image computed by using the green component of fundus photograph is proposed. In addition, image enhancement by unsharp masking (UM) is utilized for preprocessing before calculating the entropy images. The bichannel CNN incorporating the features of both the entropy images of the gray level and the green component preprocessed by UM is also proposed to improve the detection performance of referable DR by deep learning.

Automated Detection and Diagnosis of Diabetic Retinopathy: A Comprehensive Survey

“…There are additional studies that do not focus on proposing new network architectures but enhance the preprocessing step. The study done by Pao et al [ 84 ] presents bi-channel customized CNN in which an image enhancement technique known as unsharp mask is used. The enhanced images and entropy images are used as the inputs of a CNN with 4 convolutional layers with results of 87.83%, 77.81%, 93.88% over ACC, SE, and SP.…”

“…There are additional studies that do not focus on proposing new network architecture but enhance the preprocessing output. The study done by Pao et al 2020[84] presents bi-channel customized CNN in which the enhanced image using an image enhancement technique known as unsharp mask and with entropy images used as the inputs of a CNN with 4 convolutional layers with results of 87.83 %, 77.81 %, 93.88 % over ACC, SE and SP. These results are all higher than the case of analysis without preprocessing (81.80 % 68.36 %, 89.87 % respectively).Shankar et al 2020[85] proposed another approach to preprocessing using Histogram-based segmentation to extract regions containing lesions on fundus images.…”

Developments in the detection of diabetic retinopathy: a state-of-the-art review of computer-aided diagnosis and machine learning methods

“…4 and 5 , it is clearly visible that a lot of research work on the New Fundus Algorithms seemed to converge into the sole use of convolutional neural networks (Pure CNN) since 2016 (Abràmoff et al 2016 ; Prentašić and Lončarić 2016 ; Gulshan et al 2016 ; Tan et al 2017a , b ; Xu et al 2017 ; Quellec et al 2017 ; Raju et al 2017 ; Ting et al 2017 ; Mansour 2018 ; Brown et al 2018 ; Gao et al 2018 ; Gonzalez-Gonzalo et al 2020 ; Sahlsten et al 2019 ; Liu et al 2019 ; Hemanth et al 2019 ; Sun 2019 ; Zhang et al 2019 ; Li et al 2019a ; Eftekhari et al 2019 ; Bellemo et al 2019 ; Pires et al 2019 ; Qummar et al 2019 ; Zeng et al 2019 ; Mateen et al 2020 ; Wu et al 2020 ; Shaban et al 2020 ; Pao et al 2020 ; Torre et al 2020 ; Shah et al 2020 ; Zago et al 2020 ; Qiao et al 2020 ; Srivastava and Purwar 2020 ; Shankar et al 2020a ; Samanta et al 2020 ; Xie et al 2020 ; Ayhan et al 2020 ). Particularly since 2019, a tremendous increase was observed in the annual number of entries among the New Fundus Algorithms which adopted CNN as their main model of development (Gao et al 2018 ; Gonzalez-Gonzalo et al 2020 ; Sahlsten et al 2019 ; Liu et al 2019 ; Hemanth et al 2019 ; Sun 2019 ; Zhang et al 2019 ; Li et al 2019a ; Eftekhari et al 2019 ; Bellemo et al 2019 ; Pires et al 2019 ; Qummar et al 2019 ; Zeng et al 2019 ; Mateen et al …”

“…Among all entries of the New Fundus Algorithm built using pure CNN, we tabulated in Fig. 9 the number of algorithms where significant preprocessing was observed before feeding the input into CNN [2016: (Prentašić and Lončarić 2016 ; Gulshan et al 2016 ), 2017: (Tan et al 2017a , 2017b ; Xu et al 2017 ; Raju et al 2017 ), 2018: (Mansour 2018 ), 2019: (Gao et al 2018 ; Gonzalez-Gonzalo et al 2020 ; Sahlsten et al 2019 ; Liu et al 2019 ; Hemanth et al 2019 ; Zhang et al 2019 ; Li et al 2019a ; Eftekhari et al 2019 ; Pires et al 2019 ; Qummar et al 2019 ; Zeng et al 2019 ), 2020: (Mateen et al 2020 ; Wu et al 2020 ; Pao et al 2020 ; Zago et al 2020 ; Srivastava and Purwar 2020 ; Samanta et al 2020 )]. Those articles which promotes pure CNN had also given elaboration on the method of preprocessing.…”

Machine Learning for Electrocardiographic Features to Identify Left Atrial Enlargement in Young Adults: CHIEF Heart Study

“…Machine learning, an artificial intelligence (AI)-based computational statistic, has been broadly applied to clinical practice in medicine to assess disease risk and diagnosis (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12). For instance, Lin et al (12) used the support vector machine (SVM) classifier for some ECG features training successfully to identify echocardiographic left ventricular hypertrophy, and the performance of SVM was superior to the conventional ECG voltage criteria.…”