ArticleData science and engineering2021
Image Preprocessing in Classification and Identification of Diabetic Eye Diseases.
Article in Data science and engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed, 128 citations in OpenAlex.
- A novel approach for disease and pests detection in potato production system based on deep learning.Scientific reports · 2026Article
- Article
- Diabetic retinopathy severity detection using an improved Whale optimization algorithm and convolutional Kolmogorov-Arnold network.Frontiers in medicine · 2026Article
- Comparative analysis of novel preprocessing techniques and deep learning based multi modal feature fusion for diabetic retinopathy grading.Scientific reports · 2025Article
- Shedding light on pharmacoepidemiology of eye diseases: a cohort study in Western Iran.Scientific reports · 2025Article
- Advanced smart assistance with enhancing social interaction and daily activities for visually impaired individuals using deep learning with modified seagull optimization.Scientific reports · 2025Article
- Learning Architecture for Brain Tumor Classification Based on Deep Convolutional Neural Network: Classic and ResNet50.Diagnostics (Basel, Switzerland) · 2025Article
- Retina Blood Vessels Segmentation and Classification with the Multi-featured Approach.Journal of imaging informatics in medicine · 2025Article
- A novel contrast enhancement technique for diabetic retinal image pre-processing and classification.International ophthalmology · 2024Article
- Recognition of diabetic retinopathy and macular edema using deep learning.Medical & biological engineering & computing · 2024Article
- Detection and diagnosis of diabetic eye diseases using two phase transfer learning approach.PeerJ. Computer science · 2024Article
- Using nutrigenomics to guide personalized nutrition supplementation for bolstering immune system.Health information science and systems · 2023Article
- Detection and explanation of anomalies in healthcare data.Health information science and systems · 2023Article
- A mobile-based system for maize plant leaf disease detection and classification using deep learning.Frontiers in plant science · 2023Article
- An Efficient Approach to Predict Eye Diseases from Symptoms Using Machine Learning and Ranker-Based Feature Selection Methods.Bioengineering (Basel, Switzerland) · 2022Article
- A heterogeneous multi-modal medical data fusion framework supporting hybrid data exploration.Health information science and systems · 2022Article
- An assessment of random forest technique using simulation study: illustration with infant mortality in Bangladesh.Health information science and systems · 2022Article
- Automatic breast lesion segmentation in phase preserved DCE-MRIs.Health information science and systems · 2022Article
- Detection of Diabetic Retinopathy using Convolutional Neural Networks for Feature Extraction and Classification (DRFEC).Multimedia tools and applications · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diabetic eye disease (DED) is a cluster of eye problem that affects diabetic patients. Identifying DED is a crucial activity in retinal fundus images because early diagnosis and treatment can eventually minimize the risk of visual impairment. The retinal fundus image plays a significant role in early DED classification and identification. An accurate diagnostic model's development using a retinal fundus image depends highly on image quality and quantity. This paper presents a methodical study on the significance of image processing for DED classification. The proposed automated classification framework for DED was achieved in several steps: image quality enhancement, image segmentation (region of interest), image augmentation (geometric transformation), and classification. The optimal results were obtained using traditional image processing methods with a new build convolution neural network (CNN) architecture. The new built CNN combined with the traditional image processing approach presented the best performance with accuracy for DED classification problems. The results of the experiments conducted showed adequate accuracy, specificity, and sensitivity.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.