Deep Convolutional Neural Networks for Diabetic Retinopathy Classification

Lian, Chunyan; Liang, Yixiong; Kang, Rui; Xiang, Yang

doi:10.1145/3239576.3239589

Cited by 19 publications

(24 citation statements)

References 16 publications

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“…Penelitian ini membandingkan pengaruh preprocessing terhadap performa model diantaranya adalah Maria A. Bravo [8] dan Chunyan Lian [9]. Dari tinjauan pustaka juga dapat dilihat bahwa pendekatan transfer learning lebih banyak digunakan jika dibandingkan dengan end to end learning.…”

Section: Pendahuluanunclassified

Pengaruh Preprocessing Terhadap Klasifikasi Diabetic Retinopathy dengan Pendekatan Transfer Learning Convolutional Neural Network

Widyaya

Budi

2021

JuTISI

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Diabetic retinopathy (DR) is eye diseases caused by diabetic mellitus or sugar diseases. If DR is detected in early stage, the blindness that follow can be prevented. Ophthalmologist or eye clinician usually decide the stage of DR from retinal fundus images. Careful examination of retinal fundus images is time consuming task and require experienced clinicians or ophthalmologist but a computer which has been trained to recognize the DR stages can diagnose and give result in real-time manner. One approach of algorithm to train a computer to recognize an image is deep learning Convolutional Neural Network (CNN). CNN allows a computer to learn the features of an image, in our case is retinal fundus image, automatically. Preprocessing is usually done before a CNN model is trained. In this study, four preprocessing were carried out. Of the four preprocessing tested, preprocessing with CLAHE and unsharp masking on the green channel of the retinal fundus image give the best results with an accuracy of 79.79%, 82.97% precision, 74.64% recall, and 95.81% AUC. The CNN architecture used is Inception v3.

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Section: Pendahuluanunclassified

Pengaruh Preprocessing Terhadap Klasifikasi Diabetic Retinopathy dengan Pendekatan Transfer Learning Convolutional Neural Network

Widyaya

Budi

2021

JuTISI

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“…Lian et al [10] explored three neural network architectures: AlexNet, ResNet-50 and VGG-16 on a dataset that was provided by EyePACS via kaggle. The dataset consists of 35,126 fundus images and distributed to five classes: normal, mild NPDR, moderate NPDR, severe NPDR and severe PDR.…”

Section: ) Convolution Neural Network Algorithmsmentioning

confidence: 99%

Automatic Classification of Preliminary Diabetic Retinopathy Stages using CNN

Khaled¹,

ElSahhar²,

El-Dine³

et al. 2021

IJACSA

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Diabetes Mellitus is one of the modern world's most prominent and dominant maladies. This condition later on leads to a menacing eye disease called Diabetic Retinopathy (DR). Diabetic Retinopathy is a retinal disease that is caused by high blood sugar levels in the retina, and can naturally progress to irreversible vision loss (blindness). The primary purpose of this imperative research is the early detection and classification of this hazardous condition, to try and prevent any threatening complications in the future. In the course of recent years, Convolutional Neural Networks (CNNs) turned out to be exceptionally famous and fruitful in solving and unraveling image processing and object detection problems for enormous datasets. Throughout this pivotal research, a model was proposed to detect the presence of (DR) and classify it into 5 distinct stages, factoring in an immense and substantial dataset. The model starts by applying preprocessing techniques such as normalization, to maintain the same dimensions for all the images before proceeding to the main processing stage. Furthermore, diverse sampling methods such as "Resize & Crop", "Rotation", and "Flipping" have been tested out, so as to pinpoint the best augmentation technique. Finally, the normalized images were fed into a Convolutional Neural Network (CNN), to predict whether a person suffers from DR or not, and classify the level/stage of the disease. The proposed method was utilized on 88,700 retinal fundus images, which are a parcel of the full (EyePACS) dataset, and finally achieved 81.12%, 89.16%, and 84.16% for sensitivity, specificity, and accuracy, respectively.

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“…The Resnet50 was put forward by Kaiming [9]. Resnet has successfully trained 152 deep neural networks to win the ILSVRC 2015 championship and achieving 3,57% in error rate classification for the top 5 classes [9]. Resnet50 is a convolutional neural network that contains 50 layers deep.…”

Section: Resnet50mentioning

confidence: 99%

“…In Lian et al experiment with diabetic retinopathy detection, the VGG16 network model was used to classify eye images to detect illnesses caused by complications of diabetes, resulting in an accuracy of 48.13% using randomly initialized parameters. Still, after classified using hyperparameter tuning, VGG16 achieved an accuracy of 93.17% [9].…”

Section: Vgg16mentioning

confidence: 99%