Image Quality Classification for DR Screening Using Convolutional Neural Networks

Tennakoon, Ruwan; Mahapatra, Dwarikanath; Roy, Pranab; Sedai, Suman; Garnavi, Rahil

doi:10.17077/omia.1054

Cited by 29 publications

(21 citation statements)

References 11 publications

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“…Blind IQA has become more generalizable with the development of machine learning and does not require additional information beyond the original data [14]. As such, it is commonly used for fundus images classification [15][16][17][18][19][20][21] and has proven to be effective in eliminating low-quality images. Few studies have investigated the use of blind IQA methods based on OCT until 2019.…”

Section: Introductionmentioning

confidence: 99%

Deep learning for quality assessment of retinal OCT images

Wang

Deng

et al. 2019

Biomed. Opt. Express

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Optical coherence tomography (OCT) is a promising high-speed, non-invasive imaging modality providing high-resolution retinal scans. However, a variety of external factors such as light occlusion and patient movement can seriously degrade OCT image quality, which complicates manual retinopathy detection and computer-aided diagnosis. As such, this study first presents an OCT image quality assessment (OCT-IQA) system, capable of automatic classification based on signal completeness, location, and effectiveness. Four CNN architectures (VGG-16, Inception-V3, ResNet-18, and ResNet-50) from the ImageNet classification task were used to train the proposed OCT-IQA system via transfer learning. The ResNet-50 with the best performance was then integrated into the final OCT-IQA network. The usefulness of this approach was evaluated using retinopathy detection results. A retinopathy classification network was first trained by fine-tuning Inception-V3 model. The model was then applied to two test datasets, created randomly from the original dataset, one of which was screened by the OCT-IQA system and only included high quality images while the other was mixed by high and low quality images. Results showed that retinopathy detection accuracy and area under curve (AUC) were 3.75% and 1.56% higher, respectively, for the filtered data (compared with the unfiltered data). These experimental results demonstrate the effectiveness of the proposed OCT-IQA system and suggest that deep learning could be applied to the design of computer-aided systems (CADSs) for automatic retinopathy detection.

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Section: Introductionmentioning

confidence: 99%

Deep learning for quality assessment of retinal OCT images

Wang

Deng

et al. 2019

Biomed. Opt. Express

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“…Using the advantages of CNNs, Yu et al [24] presented a deep learning-based architecture that fuses the features extracted from convolution neural networks (CNN) and saliency map to classify the fundus images into two categories of quality. Similarly, Tennakoon et al [25] also presented a shallow CNN network with four convolution and two fully connected layers for two-class retinal quality classification. Recently, Zago et al [26] and Chalakkal et al [27] have used the virtues of pretrained model architectures (GoogLeNet [28], AlexNet [29], and ResNet [30]) to classify fundus images into two categories.…”

Section: ) Machine and Deep Learning Based Methodsmentioning

confidence: 99%

Multivariate Regression-Based Convolutional Neural Network Model for Fundus Image Quality Assessment

et al. 2020

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Objectively assessing the perceptual quality of an ocular fundus image is essential for the reliable diagnosis of various ocular diseases. A fair amount of work has been done in this field to date. However, the generalizability of the current work is limited, as the existing quality models were developed and evaluated with data-sets built with limited subjective inputs. This paper aims at addressing this limitation with the following two contributions. First, a new fundus image quality assessment (FIQuA) data-set is presented, containing 1500 fundus images with three classes of quality: Good, Fair, and Poor. Also, for each image, subjective scores (in the range [0-10]) were collected for six quality parameters, including structural and generic properties of the fundus images. Second, a new multivariate regression based convolutional neural network (CNN) model is proposed to predict the fundus image quality. The proposed model consists of two individually trained blocks. The first block consists of four pre-trained models, trained against the subjective scores for the six quality parameters, and aims at deriving the optimized features for classification. Next, the optimized features from each of the four models are ensembled together and transferred to the second block for final classification. The proposed model achieves a strong correlation with the subjective scores, with the values 0.941, 0.954, 0.853, and 0.401 obtained for SROCC, LCC, KCC, and RMSE respectively. Its classification accuracy is 95.66% over the FIQuA data-set, and 98.96% and 88.43% respectively over the two publicly available data-sets DRIMDB and EyeQ. INDEX TERMS Fundus image quality assessment, diabetic retinopathy, multivariate regression, convolutional neural network.

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“…The last pooling layer is applied to the output of the fifth convolutional layer. Data augmentation and dropout are the techniques utilized to avoid overfitting (BVLC-Alexnet; Tennakoon, Mahapatra, Ro, Sedai, & Garnavi, 2016;Krizhevsky et al, 2012).…”

Section: Methodsmentioning

confidence: 99%

Comparison of convolutional neural network models for food image classification

Yıgıt

Özyildirim

2018

Journal of Information and Telecommunication

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According to some estimates of World Health Organization, in 2014, more than 1.9 billion adults were overweight. About 13% of the world's adult population were obese. 39% of adults were overweight. The worldwide prevalence of obesity more than doubled between 1980 and 2014. Nowadays, mobile applications recording food intake of people become popular. If an improved food classification system is introduced, users take the photo of their meals and system classifies photos into the categories. Hence, we proposed a deep convolutional neural network structure trained from scratch and compared its performance with pre-trained structures Alexnet and Caffenet in INISTA 2017. This study is the extended version of it. Three different deep convolutional neural networks were trained from scratch by using different learning methods: stochastic gradient descent, Nesterov's accelerated gradient and Adaptive Moment Estimation, and compared with Alexnet and Caffenet fine-tuned with the same learning algorithms. Train, validation and test datasets were generated from Food11 and Food101 datasets. All tests were implemented through NVIDIA Digit interface on GeForce GTX1070. According to the test results, although pre-trained models provided better results than proposed structures, their performances were comparable. Moreover, learning optimization methods accelerated and improved the performances of all the compared models.

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Image Quality Classification for DR Screening Using Convolutional Neural Networks

Cited by 29 publications

References 11 publications

Deep learning for quality assessment of retinal OCT images

Deep learning for quality assessment of retinal OCT images

Multivariate Regression-Based Convolutional Neural Network Model for Fundus Image Quality Assessment

Comparison of convolutional neural network models for food image classification

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