DRSegNet: A cutting-edge approach to Diabetic Retinopathy segmentation and classification using parameter-aware Nature-Inspired optimization DOI Creative Commons

Sundreen Asad Kamal,

Youtian Du,

Majdi Khalid

et al.

PLoS ONE, Journal Year: 2024, Volume and Issue: 19(12), P. e0312016 - e0312016

Published: Dec. 5, 2024

Diabetic retinopathy (DR) is a prominent reason of blindness globally, which diagnostically challenging disease owing to the intricate process its development and human eye’s complexity, consists nearly forty connected components like retina, iris, optic nerve, so on. This study proposes novel approach identification DR employing methods such as synthetic data generation, K- Means Clustering-Based Binary Grey Wolf Optimizer (KCBGWO), Fully Convolutional Encoder-Decoder Networks (FCEDN). achieved using Generative Adversarial (GANs) generate high-quality transfer learning for accurate feature extraction classification, integrating these with Extreme Learning Machines (ELM). The substantial evaluation plan we have provided on IDRiD dataset gives exceptional outcomes, where our proposed model 99.87% accuracy 99.33% sensitivity, while specificity 99. 78%. why outcomes presented can be viewed promising in terms further diagnosis, well creating new reference point within framework medical image analysis providing more effective timely treatments.

Language: Английский

A systematic review of the blockchain application in healthcare research domain: toward a unified conceptual model DOI Creative Commons
Şeyma Cihan, Nebi Yılmaz, Adnan Özsoy

et al.

Medical & Biological Engineering & Computing, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 10, 2025

Abstract Recently, research on blockchain applications in the healthcare domain has attracted increasing attention due to its strong potential. However, existing literature reveals limited studies defining use cases of clinical research, categorizing and comparing available studies. Therefore, this study aims explore significant potential through a comprehensive systematic review (SLR). To thoroughly investigate all aspects subject, we analyzed primary based questions (RQs) developed unified conceptual model using step-based creation . Studies from 2015 2023 were reviewed, 34 comprehensively by PICO template. In our findings, privacy emerged as most frequently cited requirement research. The mentioned for are ensuring data immutability security A issue identified beyond common limitations capacity scalability is lack standards compliance with legal frameworks like GDPR HIPAA. After these efforts, model, which, best knowledge, first support software developers researchers developing blockchain-based platforms efficiently. Graphical abstract

Language: Английский

Citations

0
Optimizing deep learning models for glaucoma screening with vision transformers for resource efficiency and the pie augmentation method DOI Creative Commons

S. Sangchocanonta,

Pakinee Pooprasert,

Nichapa Lerthirunvibul

et al.

PLoS ONE, Journal Year: 2025, Volume and Issue: 20(3), P. e0314111 - e0314111

Published: March 21, 2025

Glaucoma is the leading cause of irreversible vision impairment, emphasizing critical need for early detection. Typically, AI-based glaucoma screening relies on fundus imaging. To tackle resource and time challenges in with convolutional neural network (CNN), we chose Data-efficient image Transformers (DeiT), a transformer, known its reduced computational demands, preprocessing decreased by factor 10. Our approach utilized meticulously annotated GlauCUTU-DATA dataset, curated ophthalmologists through consensus, encompassing both unanimous agreement (3/3) majority (2/3) data. However, DeiT’s performance was initially lower than CNN. Therefore, introduced “pie method," an augmentation method aligned ISNT rule. Along employing polar transformation to improved cup region visibility alignment transformer’s input elevated levels. The classification results demonstrated improvements comparable Using 3/3 data, excluding superior nasal regions, especially suspects, sensitivity increased 40.18% from 47.06% 88.24%. average area under curve (AUC) ± standard deviation (SD) glaucoma, no were 92.63 4.39%, 92.35 92.32 1.45%, respectively. With 2/3 temporal diagnosing 11.36% 47.73% 59.09%. AUC SD 68.22 4.45%, 68.23 73.09 3.05%, For datasets, values 84.53%, 84.54%, 91.05%, respectively, which CNN model that achieved 84.70%, 84.69%, 93.19%, Moreover, incorporation attention maps DeiT facilitated precise localization clinically significant areas, such as disc rim notching, thereby enhancing overall effectiveness screening.

Language: Английский

Citations

0
Applying a Hybrid Gray Wolf‐Enhanced Whale Optimization Algorithm to the Capacitated Vehicle Routing Problem DOI Creative Commons
Vu Hong Son Pham, Nguyễn Văn Nam, Nghiep Trinh Nguyen Dang

et al.

Journal of Advanced Transportation, Journal Year: 2025, Volume and Issue: 2025(1)

Published: Jan. 1, 2025

The study presents a novel hybrid gray wolf and whale optimization algorithm (hGWOAM) for the capacitated vehicle routing problem (CVRP). By integrating enhanced (EWOA) optimizer (GWO) with tournament selection, opposition‐based learning, mutation techniques, hGWOAM enhances efficiency under capacity constraints. Computational evaluations demonstrate its superior performance, achieving lower percentage deviations (%dev) compared to existing algorithms across multiple case studies real‐world applications. In Case Study 1, achieved mean deviation than EWOA (0.89%), GWO (0.74%), SCA (0.59%), DA (1.63%), ALO (2.26%), MHPSO (1.85%), PSO (1.96%), DPGA (2.85%), SGA (4.14%). 2, outperformed (12.05%), (2.53%), (21.07%), (17.58%). application, it best %dev, surpassing (6.64%), (6.34%), (9.01%), (12.24%). These findings highlight hGWOAM’s potential optimizing logistics, reducing operational costs, minimizing environmental impact while also paving way future advancements in metaheuristic optimization.

Language: Английский

Citations

0
Hybrid human-artificial intelligence system for early detection and classification of AMD from fundus image DOI
Imen Kallel,

Sonda Kammoun

Signal Image and Video Processing, Journal Year: 2024, Volume and Issue: 18(5), P. 4779 - 4796

Published: April 12, 2024

Language: Английский

Citations

3
Optimized feature selection for enhanced accuracy in knee osteoarthritis detection and severity classification with machine learning DOI

Anandh Sam Chandra Bose,

C. Srinivasan,

S Immaculate Joy

et al.

Biomedical Signal Processing and Control, Journal Year: 2024, Volume and Issue: 97, P. 106670 - 106670

Published: Aug. 10, 2024

Language: Английский

Citations

3
Optimising deep learning models for ophthalmological disorder classification DOI Creative Commons

S. Vidivelli,

P. Padmakumari,

Chembian Parthiban

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 24, 2025

Abstract Fundus imaging, a technique for recording retinal structural components and anomalies, is essential observing identifying ophthalmological diseases. Disorders such as hypertension, glaucoma, diabetic retinopathy are indicated by alterations in the optic disc, blood vessels, fovea, macula. Patients frequently deal with various conditions either one or both eyes. In this article, we have used different deep learning models categorisation of disorders into multiple classes labels utilising transfer learning-based convolutional neural network (CNN) methods. The Ocular Disease Intelligent Recognition (ODIR) database experiments, it contains fundus images patient’s left right We compared performance two optimisers, Stochastic Gradient Descent (SGD) Adam, separately. best result was achieved using MobileNet model Adam optimiser, yielding testing accuracy 89.64%.

Language: Английский

Citations

0
ADVANCED GENETIC ALGORITHM (GA)-INDEPENDENT COMPONENT ANALYSIS (ICA) ENSEMBLE MODEL FOR PREDICTING TRAPPED HUMANS THROUGH HYBRID DIMENSIONALITY REDUCTION DOI Creative Commons

Enoch Adama Jiya,

Ilesanmi B. Oluwafemi

Scientific African, Journal Year: 2025, Volume and Issue: unknown, P. e02564 - e02564

Published: Jan. 1, 2025

Language: Английский

Citations

0
Glaucoma Detection from Retinal Fundus Images using Graph Convolution Based Multi-Task Model DOI Creative Commons
Satyabrata Lenka, Zefree Lazarus Mayaluri,

Ganapati Panda

et al.

e-Prime - Advances in Electrical Engineering Electronics and Energy, Journal Year: 2025, Volume and Issue: unknown, P. 100931 - 100931

Published: Feb. 1, 2025

Language: Английский

Citations

0
Glaucoma diagnosis using Gabor and entropy coded Sine Cosine integration in adaptive partial swarm optimization-based FAWT DOI
Rajneesh Kumar Patel, Nancy Kumari, Siddharth Singh Chouhan

et al.

Biomedical Signal Processing and Control, Journal Year: 2025, Volume and Issue: 107, P. 107832 - 107832

Published: March 26, 2025

Language: Английский

Citations

0
HybridGWOSPEA2ABC: a novel feature selection algorithm for gene expression data analysis and cancer classification DOI
Ashimjyoti Nath, Chandan Jyoti Kumar, Sanjib Kr. Kalita

et al.

Computer Methods in Biomechanics & Biomedical Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 22

Published: April 26, 2025

DNA micro-array technology has a remarkable impact on biological research, particularly in categorizing and diagnosing cancer studying gene features functions. With the availability of extensive collections cancer-related data, there been an increased focus developing optimized Machine Learning (ML) techniques for classification through pattern analysis identification specific genes type categorization. The relevant selection treating poses significant challenge, which requires efficient feature methods. This study introduces novel hybrid algorithm, selection, integrating Grey Wolf Optimizer (GWO), Strength Pareto Evolutionary Algorithm 2 (SPEA2), Artificial Bee Colony (ABC). combination uses intelligence evolutionary computation to enhance solution diversity, convergence efficiency, exploration exploitation capabilities high-dimensional expression data. algorithm was compared with five bio-inspired algorithms using different classifiers various datasets validate its effectiveness selection. HybridGWOSPEA2ABC demonstrated superior performance identifying biomarkers conventional algorithms. Comparison benchmark shown approach's enhanced capability addressing challenges data advancing problem classification. hybridization enhances by maintaining efficiently converging optimal solutions, improving search space. provides better understanding promotes effective methodologies disease detection

Language: Английский

Citations

0