{"id":28745,"date":"2019-09-25T10:06:32","date_gmt":"2019-09-25T10:06:32","guid":{"rendered":"http:\/\/biomedpharmajournal.org\/?p=28745"},"modified":"2020-04-22T11:08:01","modified_gmt":"2020-04-22T11:08:01","slug":"detection-of-multi-class-retinal-diseases-using-artificial-intelligence-an-expeditious-learning-using-deep-cnn-with-minimal-data","status":"publish","type":"post","link":"https:\/\/biomedpharmajournal.org\/staging\/vol12no3\/detection-of-multi-class-retinal-diseases-using-artificial-intelligence-an-expeditious-learning-using-deep-cnn-with-minimal-data\/","title":{"rendered":"Detection of Multi-Class Retinal Diseases Using Artificial Intelligence: An Expeditious Learning Using Deep CNN with Minimal Data"},"content":{"rendered":"

Introduction<\/strong><\/p>\n

There are a number of retinal diseases reported so far such as Arteriosclerotic retinopathy (AR), Central retinal vein occlusion (CRVO), Central retinal artery occlusion (CRAO), Branch retinal vein occlusion (BRVO), Branch retinal artery occlusion (BRAO), Coat’s disease (CD), Hemi-Central Retinal Vein Occlusion (HRVO), Histoplasmosis (HP), Hypertensive retinopathy (HR), Choroidal neovascularization (CNV) and diabetic retinopathy (DR), which may even lead to permanent vision loss. Out of these, age-related macular degeneration (AMD) and diabetic retinopathy have been identified as the most significant.1, 2<\/sup><\/p>\n

The restrictions of the human eye and inadequacy of the conventional techniques to diagnose the various types of retinal diseases accurately and early in advance are the major challenges faced by the present-day ophthalmologists in the correct treatment of the patients.2-4 <\/sup>At present, highly sophisticated and dependable diagnostic imaging techniques such as ‘Fluorescent Retinal Angiography (FRA) and Optical Coherence Tomography (OCT) etc. are very popular. A countless number of machine learning approaches such as the artificial neural network (ANN), K-nearest neighbor algorithm, support vector machine (SVM) and Naive Bayes classifier (NBC) are incorporated to improve the prediction accuracy towards the detection of retinal diseases from the fundus images. It has been reported that ‘ANN based algorithms’ are efficient in predicting glaucoma from fundus images.5-8<\/sup><\/p>\n

Traditional classification approaches depend on feature extraction and feature classification techniques designed for the specific problem based on the available knowledge of the field. Most of the algorithms used in this area encounter the challenge of having the only insufficient number of datasets for training the model through conventional machine learning techniques.9<\/sup> The launching of ‘deep learning CNN’ based algorithms makes evolutionary changes in the approach by directly identifying features from the training data without the categorical elaboration on feature extraction and classification. However, deep learning-based models are found to improve their efficiency on vigorous training using a large number of datasets.<\/sub>10<\/sup> The availability of medical images will be highly limited in most cases, causing difficulties in using deep learning-based algorithms in the field of healthcare.8, 9<\/sup> A number of open platforms and databases have been developed to store healthcare related medical images around the world. The techniques based on ‘deep extraction of information from the available images’ such as \u2018affine transformation’ and ‘rotation of the images’ have been introduced to improve the efficiency of deep learning-based algorithms.7- 10<\/sup><\/p>\n

The deep learning based prediction strategy can be extensively used in diabetic retinopathy (DR).9<\/sup> An unconventional and evolutionary deep learning prediction model for diagnosing DR by an automatic feature extraction learning method has been developed by Google, which helps even to grade and classify the intensity of nuclear cataract among the patients.7-10<\/sup> Similarly, many deep learning approaches have been introduced in the prediction of ‘Retinopathy of prematurity (ROP)’ and AMD. Recent studies on fundus images using deep learning algorithms establish the possibility of predicting cardiovascular risk factors, suggesting these images as potential predictive models. Due to the lack of the accessible patient database, most of the predictive models designed so far have been set only for binary classification or to identify the presence of only diabetic retinopathy.9-11<\/sup><\/p>\n

The possibility of using fundus images for the design of ‘multi categorical predictive model’ covering various retinal diseases has been tried in the present work. The designed model is used to diagnose and differentiate retinal diseases such as arteriosclerotic retinopathy, branch retinal vein occlusion, etc.<\/p>\n

Methods<\/strong><\/p>\n

Data Collection<\/strong><\/p>\n

The fundus images corresponding to different retinal disease were acquired from three standard online databases namely, ‘DIARETDB0, HRF Image Database and STARE’ for the primary training of the model. Similarly, 91 real-time images were acquired and clinically diagnosed and categorized by practising ophthalmologists, for training and cross-validation.<\/p>\n

A total of 130 images were acquired from DIARETDB0, (110 are of diabetic retinopathy – haemorrhages, soft exudates, hard exudates, neovascularization and micro aneurysm] and 20 are from normal healthy people.12<\/sup> From \u2018High-Resolution Fundus (HRF) Image Database\u2019 15 images (normal, DR and glaucoma) were obtained.13<\/sup> The images have been collected for the twelve major classes of diseases such as AR, CRVO, CRAO, BRVO, BRAO, CD, HRVO, HP, HR, CN, DR and normal retina. Additionally, the prediction models have been trained efficiently with minimal retinal images and have been cross-validated using real-time fundus image of patients from the hospital.<\/p>\n

Similarly, from the ‘Structured Analysis of the Retina (STARE) online database, 14 categories of 397 images including ‘Background Diabetic Retinopathy (BDR)’, ‘Proliferative Diabetic Retinopathy (PDR)’, retinitis, emboli, CRAO, CRVO, BRAO, etc. were collected.14<\/sup><\/p>\n

The images were collected from various databases to improve the availability of various classes for analysis. The images were rotated to different angles and the corresponding data were also included in the samples to be tested leading to an increase in the effective number of images to 2484. Out of these images, 80% has been used for training and the remaining 20% for testing. The images were then resized to 224 x 224 pixels for optimum image resolution15- 18 <\/sup>the detailed information regarding data collection and dataset preparation has been included in Table 1.<\/p>\n

Table 1: Detailed Information Regarding Data Collection<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Sl. No<\/strong><\/td>\nName of the disease<\/strong><\/td>\nDIRETDB0<\/strong><\/td>\nHRF retinal database<\/strong><\/td>\nSTARE<\/strong><\/td>\nReal time images<\/strong><\/td>\n<\/tr>\n
1<\/td>\nArteriosclerotic retinopathy [AR]<\/td>\n0<\/td>\n0<\/td>\n20<\/td>\n\u00a00<\/td>\n<\/tr>\n
2<\/td>\nCentral retinal vein occlusion [CRVO]<\/td>\n0<\/td>\n0<\/td>\n25<\/td>\n\u00a014<\/td>\n<\/tr>\n
3<\/td>\nCentral retinal artery occlusion [CRAO]<\/td>\n0<\/td>\n0<\/td>\n8<\/td>\n\u00a09<\/td>\n<\/tr>\n
4<\/td>\nBranch retinal vein occlusion [BRVO]<\/td>\n0<\/td>\n0<\/td>\n10<\/td>\n\u00a025<\/td>\n<\/tr>\n
5<\/td>\nBranch retinal artery occlusion [BRAO]<\/td>\n0<\/td>\n0<\/td>\n5<\/td>\n\u00a00<\/td>\n<\/tr>\n
6<\/td>\nCoat\u2019s disease [CD]<\/td>\n0<\/td>\n0<\/td>\n10<\/td>\n\u00a00<\/td>\n<\/tr>\n
7<\/td>\nHemi-Central Retinal Vein Occlusion [HRVO]<\/td>\n0<\/td>\n0<\/td>\n10<\/td>\n\u00a00<\/td>\n<\/tr>\n
8<\/td>\nHistoplasmosis [HP]<\/td>\n0<\/td>\n0<\/td>\n10<\/td>\n\u00a00<\/td>\n<\/tr>\n
9<\/td>\nHypertensive retinopathy [HR]<\/td>\n0<\/td>\n0<\/td>\n20<\/td>\n\u00a016<\/td>\n<\/tr>\n
10<\/td>\nChoroidal neovascularization [CN]<\/td>\n0<\/td>\n0<\/td>\n50<\/td>\n\u00a027<\/td>\n<\/tr>\n
11<\/td>\ndiabetic retinopathy[DR]<\/td>\n110<\/td>\n15<\/td>\n75<\/td>\n\u00a00<\/td>\n<\/tr>\n
12<\/td>\nNormal<\/td>\n20<\/td>\n15<\/td>\n36<\/td>\n\u00a00<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n


\nDeep Learning Architecture<\/strong><\/p>\n

The deep predictive model has been designed based on a pre-trained model, which was developed by Oxford Visual Geometry Group [VGG] known as VGG19. The VGG pre-trained model is built on a 3\u00d73 convolutional layers stacked up together to increase the depth, followed by a max-pooling layer to reduce the volume size. After convolution, these features are more readily learned by a fully connected neural network of 4,096 nodes.19\u00a0<\/sup>The learning weights have been optimized to achieve maximum accuracy. The model architecture used for the research has been shown in [Fig. 1].<\/p>\n\n\n\n
\u00a0\"Figure<\/td>\nFigure 1: Deep CNN architecture<\/strong><\/p>\n

Click here to view Figure<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n

Model Optimization<\/strong><\/p>\n

Initially, the number of the epoch was optimized as 50 to provide proper convergence and maximum accuracy. A loss function or scoring function permits the system to compute the efficiency of the classification. In addition, a categorical cross entropy loss function has been used for the evaluation of the VGG19-softmax model by predicting the accuracy and validation accuracy.20-22<\/sup> Besides computing accuracy, sensitivity, specificity, and precision have been taken into account and a confusion matrix has been generated for calculating the true positive rate (TPR) and false positive rate (FPR), which reflect the detailed performance information of the classifier.21- 25<\/sup><\/p>\n

Results and Discussion<\/strong><\/p>\n

The prediction model gave an accuracy of 95.63 % and validation accuracy of 92.99 %, supporting its effectiveness in the prediction from the image samples [Fig. 2].<\/p>\n\n\n\n
\"Figure<\/td>\nFigure 2: Results of overall accuracy vs validation accuracy<\/strong><\/p>\n

Click here to view Figure<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n

A confusion matrix has been computed for further evaluation of the classifiers [Table 2]. It has been found that the model predicts most of the diseases accurately especially, BRAO, BRVO, CRAO, CD, DR, HRVO, HP, HR, and CN. However, the prediction accuracy of the model to the diseases AR and CRVO from the fundus images collected was very low [Fig. 3].<\/p>\n

Table 2: Confusion Matrix Result of Our Model<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
N=499<\/strong><\/td>\nAR<\/strong><\/td>\nBRAO<\/strong><\/td>\nBRVO<\/strong><\/td>\nCRAO<\/strong><\/td>\nCRVO<\/strong><\/td>\nCD<\/strong><\/td>\nDR<\/strong><\/td>\nHRVO<\/strong><\/td>\nHP<\/strong><\/td>\nHR<\/strong><\/td>\nCN<\/strong><\/td>\nNormal<\/strong><\/td>\n\u00a0<\/strong><\/td>\n<\/tr>\n
AR<\/strong><\/td>\n15<\/strong><\/td>\n3<\/td>\n0<\/td>\n0<\/td>\n0<\/td>\n0<\/td>\n0<\/td>\n0<\/td>\n0<\/td>\n0<\/td>\n0<\/td>\n0<\/td>\n18<\/td>\n<\/tr>\n
BRAO<\/strong><\/td>\n0<\/td>\n35<\/strong><\/td>\n0<\/td>\n0<\/td>\n0<\/td>\n0<\/td>\n0<\/td>\n0<\/td>\n0<\/td>\n0<\/td>\n0<\/td>\n0<\/td>\n35<\/td>\n<\/tr>\n
BRVO<\/strong><\/td>\n0<\/td>\n0<\/td>\n34<\/strong><\/td>\n0<\/td>\n0<\/td>\n0<\/td>\n1<\/td>\n0<\/td>\n0<\/td>\n0<\/td>\n0<\/td>\n0<\/td>\n35<\/td>\n<\/tr>\n
CRAO<\/strong><\/td>\n7<\/td>\n0<\/td>\n0<\/td>\n32<\/strong><\/td>\n0<\/td>\n0<\/td>\n1<\/td>\n0<\/td>\n0<\/td>\n0<\/td>\n0<\/td>\n0<\/td>\n40<\/td>\n<\/tr>\n
CRVO<\/strong><\/td>\n0<\/td>\n0<\/td>\n0<\/td>\n0<\/td>\n41<\/strong><\/td>\n0<\/td>\n0<\/td>\n0<\/td>\n0<\/td>\n0<\/td>\n0<\/td>\n0<\/td>\n41<\/td>\n<\/tr>\n
CD<\/strong><\/td>\n0<\/td>\n0<\/td>\n0<\/td>\n0<\/td>\n0<\/td>\n33<\/strong><\/td>\n0<\/td>\n0<\/td>\n0<\/td>\n0<\/td>\n0<\/td>\n0<\/td>\n33<\/td>\n<\/tr>\n
DR<\/strong><\/td>\n5<\/td>\n0<\/td>\n0<\/td>\n1<\/td>\n0<\/td>\n0<\/td>\n54<\/strong><\/td>\n0<\/td>\n0<\/td>\n0<\/td>\n1<\/td>\n0<\/td>\n61<\/td>\n<\/tr>\n
HRVO<\/strong><\/td>\n0<\/td>\n0<\/td>\n0<\/td>\n0<\/td>\n0<\/td>\n0<\/td>\n0<\/td>\n40<\/strong><\/td>\n0<\/td>\n0<\/td>\n0<\/td>\n0<\/td>\n40<\/td>\n<\/tr>\n
HP<\/strong><\/td>\n0<\/td>\n0<\/td>\n0<\/td>\n0<\/td>\n0<\/td>\n0<\/td>\n0<\/td>\n0<\/td>\n40<\/strong><\/td>\n0<\/td>\n0<\/td>\n0<\/td>\n40<\/td>\n<\/tr>\n
HR<\/strong><\/td>\n4<\/td>\n0<\/td>\n0<\/td>\n1<\/td>\n0<\/td>\n0<\/td>\n0<\/td>\n0<\/td>\n0<\/td>\n56<\/strong><\/td>\n1<\/td>\n0<\/td>\n62<\/td>\n<\/tr>\n
CN<\/strong><\/td>\n0<\/td>\n0<\/td>\n0<\/td>\n2<\/td>\n0<\/td>\n3<\/td>\n0<\/td>\n0<\/td>\n0<\/td>\n0<\/td>\n48<\/strong><\/td>\n0<\/td>\n53<\/td>\n<\/tr>\n
Normal<\/strong><\/td>\n4<\/td>\n0<\/td>\n0<\/td>\n0<\/td>\n1<\/td>\n0<\/td>\n0<\/td>\n0<\/td>\n0<\/td>\n0<\/td>\n0<\/td>\n36<\/strong><\/td>\n41<\/td>\n<\/tr>\n
<\/td>\n35<\/td>\n38<\/td>\n34<\/td>\n36<\/td>\n42<\/td>\n36<\/td>\n56<\/td>\n40<\/td>\n40<\/td>\n56<\/td>\n50<\/td>\n36<\/td>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n\n
\u00a0\"Figure<\/td>\nFigure 3: Accuracy results of all the categories value of our VGG19-softmax model.<\/strong><\/p>\n

Click here to view Figure<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n

The true positive rate (TPR), a measure of sensitivity has been plotted out of the confusion matrix as shown in [Fig. 4].<\/p>\n

 <\/p>\n\n\n\n
\"Figure<\/td>\nFigure 4: TPR results of all the categories value of our VGG19-softmax model.<\/strong><\/p>\n

Click here to view Figure<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n

The sensitivity score of all the diseases was found to be high, excepting AR. In a few cases such as BRVO, HR, HRVO, HP and Normal, the sensitivity was 100%. Moreover, the false positive rate of these classes is zero [Fig. 5].<\/p>\n

 <\/p>\n\n\n\n
\u00a0\"Figure<\/td>\nFigure 5: FPR results of all the categories value of our VGG19-softmax model.<\/strong><\/p>\n

Click here to view Figure<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n

The precision values of these classes are found to be high [Fig. 6] suggesting a low level of misclassification. The specificity, which is a measure of the percentage of negatives that have been correctly identified, for the “multi-class retinal disease prediction model” has been found to be greater than 95%, supporting minimum misclassification [Fig. 7]. F1-score of the classes has been shown in [Fig. 8].<\/p>\n

 <\/p>\n\n\n\n
\u00a0\"Figure<\/td>\nFigure 6: Precision results of all the categories value of our VGG19-softmax model for multi class retinal diseases.<\/strong><\/p>\n

Click here to view Figure<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n\n
\u00a0\"Figure<\/td>\nFigure 7: Results of specificity value of our VGG19-softmax model for multi-class retinal diseases classification problems.<\/strong><\/p>\n

Click here to view Figure<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n\n
\u00a0\"Figure<\/td>\nFigure 8: Results of F1-score of the \u201cVGG19-softmax\u201d model for multiclass retinal diseases classification problems.<\/strong><\/p>\n

Click here to view Figure<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n

All the parameters characterizing the prediction model are included in [Table 3]. The designed prediction tool is found to be a \u2018potential multi-class retinal disease prediction model’ from fundus images.<\/p>\n

Table 3: results of all the parameters from confusion matrix<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Name of the classes<\/strong><\/td>\nAccuracy<\/strong><\/td>\nPrecision<\/strong><\/td>\nTPR<\/strong><\/td>\nSpecificity<\/strong><\/td>\nFPR<\/strong><\/td>\nF1-score<\/strong><\/td>\n<\/tr>\n
AR<\/td>\n95.28<\/td>\n83.33<\/td>\n42.86<\/td>\n99.34<\/td>\n0.66<\/td>\n56.60<\/td>\n<\/tr>\n
BRAO<\/td>\n99.36<\/td>\n100.00<\/td>\n92.11<\/td>\n100.00<\/td>\n0.00<\/td>\n95.89<\/td>\n<\/tr>\n
BRVO<\/td>\n99.78<\/td>\n97.14<\/td>\n100.00<\/td>\n99.77<\/td>\n0.23<\/td>\n98.55<\/td>\n<\/tr>\n
CRAO<\/td>\n97.48<\/td>\n80.00<\/td>\n88.89<\/td>\n98.18<\/td>\n1.82<\/td>\n84.21<\/td>\n<\/tr>\n
CRVO<\/td>\n99.78<\/td>\n100.00<\/td>\n97.62<\/td>\n100.00<\/td>\n0.00<\/td>\n98.80<\/td>\n<\/tr>\n
CD<\/td>\n99.36<\/td>\n100.00<\/td>\n91.67<\/td>\n100.00<\/td>\n0.00<\/td>\n95.65<\/td>\n<\/tr>\n
DR<\/td>\n98.10<\/td>\n88.52<\/td>\n96.43<\/td>\n98.32<\/td>\n1.68<\/td>\n92.31<\/td>\n<\/tr>\n
HRVO<\/td>\n100.00<\/td>\n100.00<\/td>\n100.00<\/td>\n100.00<\/td>\n0.00<\/td>\n100.00<\/td>\n<\/tr>\n
HP<\/td>\n100.00<\/td>\n100.00<\/td>\n100.00<\/td>\n100.00<\/td>\n0.00<\/td>\n100.00<\/td>\n<\/tr>\n
HR<\/td>\n98.72<\/td>\n90.32<\/td>\n100.00<\/td>\n98.55<\/td>\n1.45<\/td>\n94.92<\/td>\n<\/tr>\n
CN<\/td>\n98.51<\/td>\n90.57<\/td>\n96.00<\/td>\n98.81<\/td>\n1.19<\/td>\n93.20<\/td>\n<\/tr>\n
Normal<\/td>\n98.93<\/td>\n87.80<\/td>\n100.00<\/td>\n98.85<\/td>\n1.15<\/td>\n93.51<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n

With recent advancements in the field of medical image processing using machine learning algorithms and data mining techniques, the computer-aided medical diagnostics has become an inevitable part of healthcare. In deep learning platform, the binary class-based classifiers have shown better accuracy and performance than multiclass classifiers. The most common drawback faced by multiclass classifiers is the chance for over fitting. For the designed model, the validation accuracy is in close proximity to the training data, therefore the chances of the model to over fit is negligibly small. The lower values of FPR, the higher values of precision and specificity ensure the model to be more dependable with minimum chances for Misclassification.<\/p>\n

Conclusion<\/strong><\/p>\n

The ‘multi-class retinal disease prediction model’ is found to be a potential device in predicting retinal diseases from the fundus images. The overall efficiency of the model is found to be 92%. This study supports the possibility of exploiting the opportunities of pre-trained models with different medical applications using deep learning techniques. The model seems to predict the diseases BRAO, BRVO, CRAO, CD, DR, HRVO, HP, HR, and CN.<\/p>\n

The model could further be used to make individual variations associated with the images and various mutations corresponding to retinal diseases thereby making fundus image as a potential biomarker for various associated diseases like cardiovascular complications, Alzheimer’s disease, hypoxic conditions and etc.<\/p>\n

Acknowledgments<\/strong><\/p>\n

The authors Karthikeyan S and Sanjay Kumar P express their gratitude to \u2018Coconut Development Board\u2019, Government of India for the financial support as fellowship.<\/p>\n

Funding Source<\/strong><\/p>\n

The author(s) received no financial support for the research, authorship, and\/or publication of this article.<\/p>\n

Conflict of Interest<\/strong><\/p>\n

The authors declare that there is no conflict of interest associated with the manuscript.<\/p>\n

References <\/strong><\/p>\n

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