Efficient feature selection based novel clinical decision support system for glaucoma prediction from retinal fundus images

Medical Engineering & Physics, Journal Year: 2023, Volume and Issue: 123, P. 104077 - 104077

Published: Dec. 7, 2023

Language: Английский

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Feature subset selection through nature inspired computing for efficient glaucoma classification from fundus images

Multimedia Tools and Applications, Journal Year: 2024, Volume and Issue: 83(32), P. 77873 - 77944

Published: Feb. 23, 2024

Language: Английский

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AN EFFICIET GLAUCOMA PREDICTION AND CLASSIFICATION INTEGRATING RETINAL FUNDUS IMAGES AND CLINICAL DATA USING DnCNN WITH MACHINE LEARNING ALGORITHMS

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104220 - 104220

Published: Feb. 1, 2025

Language: Английский

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SUPER-COUGH: A Super Learner-based ensemble machine learning method for detecting disease on cough acoustic signals

Biomedical Signal Processing and Control, Journal Year: 2024, Volume and Issue: 93, P. 106165 - 106165

Published: Feb. 28, 2024

Language: Английский

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Assistance of Artificial Intelligence in Diagnosis of Vitreoretinal Lymphoma on Optical Coherence Tomography

Advanced Intelligent Systems, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 26, 2025

Vitreoretinal lymphoma (VRL) remains a diagnostic challenge due to its scarce prevalence, and delayed diagnosis usually results in blindness even fatal outcomes. Herein, an artificial intelligence (AI) system is developed identify VRL among 16 retinal diseases conditions on optical coherence tomography (OCT) images with the cross‐subject meta‐transfer learning (CS‐MTL) algorithm. Extensive experiments of few‐shot recognition tasks prove robustness our model 1‐, 3‐, 5‐shot scenarios, achieving F1 score 0.8697 0.9367. The superiority shown higher (0.9310) compared other state‐of‐the‐art algorithms (0.5487–0.9018) three doctors whose clinical experiences range between 3 10 years without help CS‐MTL (0.7773–0.8949). AI assistance significantly improves scores by 6.16–14.46% ( p < 0.001). Moreover, AI‐assisted senior doctor specialist (0.9414 0.9500), but not junior (0.8897), exceed that (0.9310). This study presents promising approach for aiding OCT may provide novel insight into collaboration techniques, resulting reducing risk delays rare diseases.

Language: Английский

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Feature selection using Hybridized Genghis Khan Shark with Snow Ablation optimization technique for Multi-Disease Prognosis

Intelligence-Based Medicine, Journal Year: 2025, Volume and Issue: unknown, P. 100249 - 100249

Published: April 1, 2025

Language: Английский

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An improved breast cancer classification with hybrid chaotic sand cat and Remora Optimization feature selection algorithm

PLoS ONE, Journal Year: 2024, Volume and Issue: 19(4), P. e0300622 - e0300622

Published: April 11, 2024

Breast cancer is one of the most often diagnosed cancers in women, and identifying breast histological images an essential challenge automated pathology analysis. According to research, global BrC around 12% all cases. Furthermore, 25% women suffer from BrC. Consequently, prediction depends critically on quick precise processing imaging data. The primary reason deep learning models are used detection that they can produce findings more quickly accurately than current machine learning-based techniques. Using a BreakHis dataset, we demonstrated this work viability automatically classifying first stage pre-processing, which employs Adaptive Switching Modified Decision Based Unsymmetrical Trimmed Median Filter (ASMDBUTMF) remove high-density noise. After image has been pre-processed, it segmented using Thresholding Level set approach. Next, propose hybrid chaotic sand cat optimization technique, together with Remora Optimization Algorithm (ROA) for feature selection. suggested strategy facilitates acquisition functionality attributes, hence simplifying procedure. Additionally, aids resolving problems pertaining optimization. Following selection, best characteristics proceed categorization A DL classifier called Conditional Variation Autoencoder discriminate between cancerous benign tumors while categorizing them. classification accuracy 99.4%, Precision 99.2%, Recall 99.1%, F- score 99%, Specificity 99.14%, FDR 0.54, FNR 0.001, FPR 0.002, MCC 0.98 NPV 0.99 were obtained proposed compared other research results our desirable.

Language: Английский

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Hybrid human-artificial intelligence system for early detection and classification of AMD from fundus image

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

Published: April 12, 2024

Language: Английский

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Impact of Physiological and Psychological Stress on Glaucoma Development and Progression: A Narrative Review

Medicina, Journal Year: 2025, Volume and Issue: 61(3), P. 418 - 418

Published: Feb. 27, 2025

Glaucoma is a leading cause of irreversible blindness worldwide. Presently, elevated intraocular pressure (IOP) the only approved modifiable risk factor. A consensus current literature suggests that both physiological and psychological stress may also impact lifelong course glaucoma. Specifically, known to influence sympathetic nervous system activity. An increase in activity elevate person’s blood (BP) IOP, are strongly associated with glaucomatous disease. Anxiety depression have more conflicting evidence relation Socioeconomic environmental worsen adherence therapy disease outcomes due lack financial resources related access healthcare. Neighborhood quality conditions, particularly urban environments, been glaucoma factors, higher prevalence, delayed surgical interventions. Racial differences identified, Black patients being stressed likely present increased severity faster progression than White patients. Mindfulness, meditation, other forms relaxation shown reduce IOP biomarkers result improved life (QOL). Larger studies diverse populations needed clarify identify best therapeutic approaches as method improve clinical QOL for

Language: Английский

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Glaucoma diagnosis using Gabor and entropy coded Sine Cosine integration in adaptive partial swarm optimization-based FAWT

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

Published: March 26, 2025

Language: Английский

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