Diabetic Retinopathy Detection &amp; Classification using Efficient Net Model

Giroti, Ishika; Das, Jeevan Kumar A; N, Harshith; Thahniyath, Gousia

doi:10.1109/icaia57370.2023.10169756

Cited by 4 publications

(2 citation statements)

References 9 publications

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“…Fewer pixels force the model to concentrate on the most important characteristics, which may improve generalization to previously unobserved data as the problem with (6) . A 256x256 image size is ideal for accurate predictions in tasks like medical imaging or satellite imagery analysis, as it captures the most relevant information that solves the problem (7) . The dataset used is labelled data.…”

Section: Resultsmentioning

confidence: 99%

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Early Assessment of Diabetic Retinopathy by Detecting Microaneurysm with Deep Neural Network

Sharma,

Pawar

2024

IJST

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Objectives: This study aims to detect early signs of diabetic retinopathy, also known as microaneurysm (MA). Early detection of MA can help in diagnosing diabetic retinopathy effectively. Methods: To achieve this objective a method is proposed which is based on a deep learning model that incorporates transfer learning. The dataset used in this study is E-Ophtha which consists of 381 high-quality images. The proposed model consists of three steps which are preprocessing, feature extraction and classification. The method uses CLAHE to enhance the details of the fundus image. The feature extraction step and the classification are done using a deep learning algorithm. The proposed method is compared by using parameters such as Recall, Precision, and accuracy. Findings: The accuracy achieved by the model is about 98.82%-100%, a recall/sensitivity of 100%, and precision is also 100% which is close to the state of the art. This implies that deep learning methods are a good fit for identifying microaneurysms and ultimately detecting diabetic retinopathy. Novelty: Early detection and diagnosis of diabetic retinopathy can be achieved with this approach, and appropriate medication can impede the disease's development. Keywords: Diabetic Retinopathy, PDR, NPDR, Image Processing, Deep Learning

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Section: Resultsmentioning

confidence: 99%

“…Longer training periods and a greater need for computational resources may arise from this. Giroti et al (7) have randomly resized the images for training which can cause loss of information of important features and can also lead to overfitting. Kapoor et al (8) used autoencoders.…”

Section: Introductionmentioning

confidence: 99%