Reducing Overfitting in Diabetic Retinopathy Detection using Transfer Learning

Barhate, Nikhil; Bhave, Sahil; Bhise, Rugved; Sutar, Rajendra G.; Karia, Deepak C.

doi:10.1109/iccca49541.2020.9250772

Cited by 8 publications

(3 citation statements)

References 2 publications

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“…Thota and Reddy ( 2020 ) used the VGG16 pre-trained neural network for the diagnosis of DR and achieved the values of Acc, Se, and Sp of 0.740, 0.80, and 0.65, respectively. Barhate et al ( 2020 ) worked using three pre-trained models, namely, VGG19, VGG16, and AlexNet. They proposed the VGG autoencoder network and worked using the EyePACS dataset.…”

Section: Literature Reviewmentioning

confidence: 99%

Smart grading of diabetic retinopathy: an intelligent recommendation-based fine-tuned EfficientNetB0 framework

Anand,

Koundal,

Alghamdi

et al. 2024

Front. Artif. Intell.

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Diabetic retinopathy is a condition that affects the retina and causes vision loss due to blood vessel destruction. The retina is the layer of the eye responsible for visual processing and nerve signaling. Diabetic retinopathy causes vision loss, floaters, and sometimes blindness; however, it often shows no warning signals in the early stages. Deep learning-based techniques have emerged as viable options for automated illness classification as large-scale medical imaging datasets have become more widely available. To adapt to medical image analysis tasks, transfer learning makes use of pre-trained models to extract high-level characteristics from natural images. In this research, an intelligent recommendation-based fine-tuned EfficientNetB0 model has been proposed for quick and precise assessment for the diagnosis of diabetic retinopathy from fundus images, which will help ophthalmologists in early diagnosis and detection. The proposed EfficientNetB0 model is compared with three transfer learning-based models, namely, ResNet152, VGG16, and DenseNet169. The experimental work is carried out using publicly available datasets from Kaggle consisting of 3,200 fundus images. Out of all the transfer learning models, the EfficientNetB0 model has outperformed with an accuracy of 0.91, followed by DenseNet169 with an accuracy of 0.90. In comparison to other approaches, the proposed intelligent recommendation-based fine-tuned EfficientNetB0 approach delivers state-of-the-art performance on the accuracy, recall, precision, and F1-score criteria. The system aims to assist ophthalmologists in early detection, potentially alleviating the burden on healthcare units.

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Section: Literature Reviewmentioning

confidence: 99%

Smart grading of diabetic retinopathy: an intelligent recommendation-based fine-tuned EfficientNetB0 framework

Anand,

Koundal,

Alghamdi

et al. 2024

Front. Artif. Intell.

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“…The model is compared with others and gain 37.50% of downturn in memory utilization, 49.34% gain in run time. The researchers [4][29] proposed a method using the combination of VGG network and auto encoder, which prevents the overfitting while training the model. VGG network is used for transfer learning purpose.…”

Section: Iibackground Studymentioning

confidence: 99%

Comparative Analysis of Early Diabetic Retinopathy Detection: Enhanced Minimal CNN vs VGG Architecture on CLAHE- Preprocessed Retinal Images

P. Ranjana

2024

jes

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Ocular impairment is one of the prominent problems affecting middle-aged individuals due to uncontrolled blood sugar levels, commonly known as Diabetic Retinopathy (DR). The small abnormalities in the retinal capillaries, called microaneurysms and intra retinal bleeding, are the initial symptoms of Diabetic Retinopathy. Clinically recognizing diabetic retinal disease is a time-consuming and difficult process due to limitations in resources and experienced doctors. Early detection is crucial in avoiding the progression of Diabetic Retinopathy, highlighting the importance of an automated DR detection method to identify symptoms in its early stages. In this paper, researchers developed an unfamiliar framework known as Enhanced Minimal Convolutional Neural Network (EMCNN) to classify Mild-DR and No-DR ophthalmic photos using a binary classification process. The proposed new model EMCNN is compared with the migration learning method using the existing framework VGG16 and VGG19 in terms of precision and effectiveness. Before being sent across the network, the fundus pictures underwent preprocessing using the Contrast Limited Adaptive Histogram Equalization (CLAHE) tactic. EMCNN is an experimental model that enjoys a minimum number of layers and batch normalization to minimize the training effort. The EMCNN model achieved 94.89% accuracy using 3100 image dataset which is a remarkable improvement when compared with VGG architectures since the VGG architecture is trained with millions of images.

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“…The process included Steps like [1] Labeling each growing of pairs of named organization. [2] Making congregate pairs of named institution.…”

Section: Related Workmentioning

confidence: 99%

Linguistic mastery: Advances in natural language processing

S.,

L.,

Priyatharsini

et al. 2023

ACE

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NLPstands for Natural Language Processing, it is a kind of artificial intelligence. That demonstrate with scrutinize, understanding, and accuse natural human languages. In such a manner that analog procedure would get in touch and human language excluding computer-propel language like programming language such as c, c++, python, javascript. NLP field contains the intellect computer programming into an understanding language which is understandable by humans. So, powerful algorithms can even interpret one language into another language scrupulously. Natural language processing occasionally also known as computational linguistics, it uses semantics and syntax. It assists computers to acknowledge how humans talk or write and also it will know how to deduce meaning of words that they are saying. A language is designate as a synchronize of rules and symbols. These Symbol are amalgamated and used for disseminate the data as well as make contact to other resources. Symbols are potentiate by the rules and regulations.

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Reducing Overfitting in Diabetic Retinopathy Detection using Transfer Learning

Cited by 8 publications

References 2 publications

Smart grading of diabetic retinopathy: an intelligent recommendation-based fine-tuned EfficientNetB0 framework

Smart grading of diabetic retinopathy: an intelligent recommendation-based fine-tuned EfficientNetB0 framework

Comparative Analysis of Early Diabetic Retinopathy Detection: Enhanced Minimal CNN vs VGG Architecture on CLAHE- Preprocessed Retinal Images

Linguistic mastery: Advances in natural language processing

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