Early Detection of Diabetic Retinopathy by Using Deep  Learning Neural Network

Johari, Mohamad Hazim; Hassan, H.; Yassin, Ahmad Ihsan Mohd; Tahir, Nooritawati Md; Zabidi, Azlee; Rizman, Zairi Ismael; Baharom, Rahimi; Wahab, Norfishah

doi:10.14419/ijet.v7i4.11.20804

Cited by 12 publications

(5 citation statements)

References 2 publications

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“…The best-reported accuracy was 63.2% for the InceptionV3 architecture. Hazim et al [60] used 580 images from the Messidor dataset to test the transfer learning of AlexNet. They opted for a two-class classification, and they cropped the images to 227 × 227.…”

Section: Paper Reviewmentioning

confidence: 99%

“…Many private datasets were used as well in studies [53,57,64,68,69]. Many researchers like Tsighe et al [65], Li et al [55], Mohammadian et al [56], Hazim et al [60], Lam et al [61], and Lam et al [62] considered a binary classification task due to the lack of a sufficient number of images for some of the classes. In particular, the lack of severe cases images plays an important role because there are too few images that are available for training the network.…”

Section: The Datasets Usedmentioning

confidence: 99%

“…The optimizer can have a huge impact on the convergence of the training process, especially for transfer learning, as pointed out by Mohammadian et al [56] and Lam et al [62]. Four optimizers were mainly reported by the authors, namely SGD for studies [52][53][54][55][56][57][58][59][60][61][62][63][64][65] SGD with momentum for studies [56,58,59], Adam for studies [56,67,70], and RMSProp in [68]. The stochastic gradient descent optimizer (SGD) allows for a faster training process than the traditional gradient descent because it only considers, at each iteration, a subset of the training set.…”

Section: The Optimizers Usedmentioning

confidence: 99%

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Transfer Learning with Convolutional Neural Networks for Diabetic Retinopathy Image Classification. A Review

2020

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Diabetic retinopathy (DR) is a dangerous eye condition that affects diabetic patients. Without early detection, it can affect the retina and may eventually cause permanent blindness. The early diagnosis of DR is crucial for its treatment. However, the diagnosis of DR is a very difficult process that requires an experienced ophthalmologist. A breakthrough in the field of artificial intelligence called deep learning can help in giving the ophthalmologist a second opinion regarding the classification of the DR by using an autonomous classifier. To accurately train a deep learning model to classify DR, an enormous number of images is required, and this is an important limitation in the DR domain. Transfer learning is a technique that can help in overcoming the scarcity of images. The main idea that is exploited by transfer learning is that a deep learning architecture, previously trained on non-medical images, can be fine-tuned to suit the DR dataset. This paper reviews research papers that focus on DR classification by using transfer learning to present the best existing methods to address this problem. This review can help future researchers to find out existing transfer learning methods to address the DR classification task and to show their differences in terms of performance.

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Section: Paper Reviewmentioning

confidence: 99%

Section: The Datasets Usedmentioning

confidence: 99%

Section: The Optimizers Usedmentioning

confidence: 99%

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Transfer Learning with Convolutional Neural Networks for Diabetic Retinopathy Image Classification. A Review

2020

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“…The replicate study showed that selecting the suitable normalization method is very critical and is hence supposed to be used in the original study. Hazim Johari et al [13] used Alexnet deep learning neural network on retinal images for DR detection by using availability public MESSIDOR database. They demonstrated that Alexnet layers is the perfect layer for deep learning.…”

Section: Related Workmentioning

confidence: 99%

Recent Innovations in Automated Detection and Classification of Diabetic Retinopathy

Aladawi¹,

Jayakumari²,

Sumesh³

et al. 2019

IJITEE

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Due to the increasing prevalence of diabetic retinopathy worldwide, it’s an urgent need to develop smart system that help to detect disease using one of the modern technologies. Artificial intelligence is one of the popular techniques nowadays which has the ability to learn from experience and carry out human-like tasks. Large number of researches have been conducted to find out effective medical diagnosis methods for numerous diseases. Likewise, huge number of researches have been done that discuss automated detection and classification of diabetic retinopathy. This paper reviews the existing methodologies, datasets, sensitivity, specificity and classification accuracy in diabetic retinopathy.

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“…GoogleNet CNN platform was used by the authors in [11] in order to detect the symptoms of diabetic macular edema (DME) utilizing the benchmark MESSIDOR and the other is the E-Ophtha datasets. AlexNet based architecture was presented by the authors in [12] using the MESSIDOR dataset for diagnostic information extraction of DR severities. A zoon-in-Net based DR diagnostic platform was built by Wang, et al [13] for retinopathy detection and severity grading.…”

Section: Introductionmentioning

confidence: 99%

An Optimized Deep Learning based Technique for Grading and Extraction of Diabetic Retinopathy Severities

Zhang

Luo²,

Cengiz

2021

IJCAI

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The prognosis of Diabetic Retinopathy (DR) requires regular eye examinations, as ophthalmologists depends on fundus segmentation to treat DR pathologies. Automated approaches for detection, segmentation and classification have developed as an imperative area of research for the effective diagnosis of DR for the treatment of serious eye conditions that prevent visual impairment. Diagnosis of various DR lesions, as well as different severities, helping the ophthalmologists to analyze variations in fundus images and take the necessary measures before the disease progresses. Deep learning techniques have evolved as a recent advent to combat the issues of conventional machine leaning based methods. An optimized deep learning framework is proposed in this article for grading and extraction of diabetic retinopathy severities. This involves various steps like background segmentation, feature set extraction, feature optimization using Cuckoo search and Convolutional Neural Network (CNN) severity grade classification. The method was validated on two standard datasets MESSIDOR and IDRiD. The proposed method yields an accuracy value of 97.55%, cross entropy loss of 0.367 and time intricacy of 20 mins and 15 secs for MESSIDOR and 98.02% cross entropy loss of 0.345 and time intricacy of 22 mins and 21 secs for IDRiD dataset; respectively. The state-of-the-art comparison depicts that the proposed CNN based method provides a maximum accuracy improvement of 10.46% comparative to the existing methodology. The proposed framework yields better accuracy by procurement of the investigative outcomes acquired exhibits proficient DR determination.Povzetek: Opisana je metoda ugotavljanja sladkorne bolezni iz slik očesnega ozadje na osnovi strojnega in globokega učenja.

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Early Detection of Diabetic Retinopathy by Using Deep Learning Neural Network

Cited by 12 publications

References 2 publications

Transfer Learning with Convolutional Neural Networks for Diabetic Retinopathy Image Classification. A Review

Transfer Learning with Convolutional Neural Networks for Diabetic Retinopathy Image Classification. A Review

Recent Innovations in Automated Detection and Classification of Diabetic Retinopathy

An Optimized Deep Learning based Technique for Grading and Extraction of Diabetic Retinopathy Severities

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