RETRACTED ARTICLE: An enhanced diabetic retinopathy detection and classification approach using deep convolutional neural network

Diabetic Retinopathy is a major cause of vision loss and blindness affecting millions of people across the globe. Although there are established screening methods - fluorescein angiography and optical coherence tomography for detection of the disease but in majority of the cases, the patients remain ignorant and fail to undertake such tests at an appropriate time. The early detection of the disease plays an extremely important role in preventing vision loss which is the consequence of diabetes mellitus remaining untreated among patients for a prolonged time period. Various machine learning and deep learning approaches have been implemented on diabetic retinopathy dataset for classification and prediction of the disease but majority of them have neglected the aspect of data pre-processing and dimensionality reduction, leading to biased results. The dataset used in the present study is a diabetes retinopathy dataset collected from the UCI machine learning repository. At its inceptions, the raw dataset is normalized using the Standardscalar technique and then Principal Component Analysis (PCA) is used to extract the most significant features in the dataset. Further, Firefly algorithm is implemented for dimensionality reduction. This reduced dataset is fed into a Deep Neural Network Model for classification. The results generated from the model is evaluated against the prevalent machine learning models and the results justify the superiority of the proposed model in terms of Accuracy, Precision, Recall, Sensitivity and Specificity.

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“…The binary value of amplitude-modulation-frequency-modulation(AM/FM)-based classification. 20 Class label. 1 -signs of DR and 0 -no signs of DR.…”

Section: Resultsmentioning

confidence: 99%

“…The study in [20] presented a hybrid technique incorporating image processing and deep learning for detection and classification of diabetic retinopathy. The model was validated using the retinal fundus dataset consisting of 400 images of the MESSIDOR database yielding good results.…”

Section: Related Workmentioning

confidence: 99%

Early Detection of Diabetic Retinopathy Using PCA-Firefly Based Deep Learning Model

et al. 2020

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“…In [28], the authors have proposed a system based on combination between image processing techniques and deep learning algorithms to diagnose DR. MESSIDOR-1 dataset of 400 images has been used where 300 images has been used for training and the remaining for testing. The problem has been handled as a binary-class classification problem (healthy/unhealthy).…”

Section:  Deep Learning For Detection Of Diabetic Retinopathymentioning

confidence: 99%

A Critical Insight into Pragmatic Manifestation of Diabetic Retinopathy Grading and Detection

Sallam¹,

Olanrewaju²,

Asnawi³

2019

IJITEE

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Nowadays, artificial intelligence applications invade all of the fields including medical applications field. Deep learning, a subfield of artificial intelligence, in particular, Convolutional Neural Networks (CNN), have quickly become the first choice for processing and analyzing medical images due to its performance and effectiveness. Diabetic retinopathy is a vision loss disease that infects people with diabetes. This disease damages the blood vessels in the retina, hence, leads to blindness. Due to the sensitivity and complications involved in managing diabetics, designing and developing automated systems to detect and grade diabetic retinopathy is considered one of the recent research areas in the world of medical image applications. In this paper, the aspects of deep learning field related to diabetic retinopathy have been discussed. Various concepts in deep learning including traditional Artificial Neural Network (ANN) algorithm, ANN drawbacks in context of computer vision and image processing applications, and the best algorithm to overcome ANN drawbacks, CNN, have been elucidated along with the architecture. The paper also reviews an extensive summary of some works in the current research trend and future applications of the DL algorithms in medical image analysis for DR detection and grading. Furthermore, various research gabs related to building such automated systems for medical image analysis have been conferred – such as imbalance dataset which is considered one of the main performance issues that should be handled, the need of high performance computational resources to train deep and efficient models and others. This is quite beneficial for researchers working in the domain of medical image analysis to handle DR.

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“…The aim of the proposed research is to develop an image evaluation scheme using the hybrid methods existing in the literature [17][18][19][20]. The Hybrid-Image-Processing-System (HIPS) can be developed by integrating the chosen multi-threshold scheme with a chosen segmentation technique.…”

Section: Introductionmentioning

confidence: 99%

Segmentation and Evaluation of COVID-19 Lesion from CT scan Slices - A Study with Kapur/Otsu Function and Cuckoo Search Algorithm

Satapathy

Hemanth

Kadry

et al. 2020

Preprint

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Infection/disease in lung is one of the acute illnesses in humans. Pneumonia is one of the major lung diseases and each year; the death rate due to the untreated pneumonia is on rise globally. From December 2019; the pneumonia caused by the Coronavirus Disease (COVID-19) has emerged as a global threat due to its rapidity. The clinical level assessment of the COVID-19 is normally performed with the Computed-Tomography scan Slice (CTS) or the Chest X-ray. This research aims to propose an image processing system to examine the COVID-19 infection in CTS. This work implements Cuckoo-Search-Algorithm (CSA) monitored Kapur/Otsu image thresholding and a chosen image segmentation procedure to extract the pneumonia infection. After extracting the COVID-19 infection from the CTS, a relative assessment is then executed with the Ground-Truth-Image (GTI) offered by a radiologist and the essential performance measures are then computed to confirm the superiority of the proposed technique. This work also presents a comparative assessment among the segmentation procedures, such as Level-Set (LS) and Chan-Vese (CV) methods. The experimental outcome authenticates that, the results by Kapur and Otsu threshold are approximately similar when the LS is implemented and the CV with the Otsu presents better values of Jaccard, Dice and Accuracy compared to other methods presented in this research.

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RETRACTED ARTICLE: An enhanced diabetic retinopathy detection and classification approach using deep convolutional neural network

Cited by 241 publications

References 42 publications

Early Detection of Diabetic Retinopathy Using PCA-Firefly Based Deep Learning Model

Early Detection of Diabetic Retinopathy Using PCA-Firefly Based Deep Learning Model

A Critical Insight into Pragmatic Manifestation of Diabetic Retinopathy Grading and Detection

Segmentation and Evaluation of COVID-19 Lesion from CT scan Slices - A Study with Kapur/Otsu Function and Cuckoo Search Algorithm

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