Advancing Visual Perception Through VCANet-Crossover Osprey Algorithm: Integrating Visual Technologies.
Diabetic retinopathy (DR) is a significant vision-threatening condition, necessitating accurate and efficient automated screening methods. Traditional deep learning (DL) models struggle to detect subtle lesions and also suffer from high computational complexity. Existing models primarily mimic the p...
| Publicado en: | Journal of Imaging Informatics in Medicine Vol. 39; no. 1; pp. 669 - 699 |
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| Autores principales: | , , |
| Formato: | algorithm equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Feb2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=191694154&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 191694154 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Feb2026 vid: 39 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 191694154 191694154 191694154 10.1007/s10278-025-01467-w 191694154 ppf: 669 ppct: 30 formats: tig: atl: Advancing Visual Perception Through VCANet-Crossover Osprey Algorithm: Integrating Visual Technologies. aug: au: Ning, Yuwen Li, Jiaxin Sun, Shuyi affil: https://ror.org/00ms48f15 Teaching and Research Support Center, Air Force Medical University, 710032, Xi'an, China sug: subj: Visual Perception Algorithms Diabetic Retinopathy Diagnosis Deep Learning Image Interpretation, Computer Assisted Automation Human Vision Screening Decision Support Systems, Clinical Bionics Time Factors Enhancement of Contrast Effect Descriptive Statistics Diabetic Patients Friedman Test Chi Square Test ab: Diabetic retinopathy (DR) is a significant vision-threatening condition, necessitating accurate and efficient automated screening methods. Traditional deep learning (DL) models struggle to detect subtle lesions and also suffer from high computational complexity. Existing models primarily mimic the primary visual cortex (V1) of the human visual system, neglecting other higher-order processing regions. To overcome these limitations, this research introduces the vision core–adapted network-based crossover osprey algorithm (VCANet-COP) for subtle lesion recognition with better computational efficiency. The model integrates sparse autoencoders (SAEs) to extract vascular structures and lesion-specific features at a pixel level for improved abnormality detection. The front-end network in the VCANet emulates the V1, V2, V4, and inferotemporal (IT) regions to derive subtle lesions effectively and improve lesion detection accuracy. Additionally, the COP algorithm leveraging the osprey optimization algorithm (OOA) with a crossover strategy optimizes hyperparameters and network configurations to ensure better computational efficiency, faster convergence, and enhanced performance in lesion recognition. The experimental assessment of the VCANet-COP model on multiple DR datasets namely Diabetic_Retinopathy_Data (DR-Data), Structured Analysis of the Retina (STARE) dataset, Indian Diabetic Retinopathy Image Dataset (IDRiD), Digital Retinal Images for Vessel Extraction (DRIVE) dataset, and Retinal fundus multi-disease image dataset (RFMID) demonstrates superior performance over baseline works, namely EDLDR, FFU_Net, LSTM_MFORG, fundus-DeepNet, and CNN_SVD by achieving average outcomes of 98.14% accuracy, 97.9% sensitivity, 98.08% specificity, 98.4% precision, 98.1% F1-score, 96.2% kappa coefficient, 2.0% false positive rate (FPR), 2.1% false negative rate (FNR), and 1.5-s execution time. By addressing critical limitations, VCANet-COP provides a scalable and robust solution for real-world DR screening and clinical decision support. pubtype: Academic Journal doctype: algorithm equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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