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

Feature subset selection through nature inspired computing for efficient glaucoma classification from fundus images DOI
Law Kumar Singh, Munish Khanna,

Rekha Singh

et al.

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

Published: Feb. 23, 2024

Language: Английский

Citations

14
A comparative study on the effectiveness of interactive versus non-interactive optic disc photograph training modules for glaucoma diagnosis among ophthalmology residents DOI Creative Commons

Thanatcha Tanjirawatana,

Kitiya Ratanawongphaibul, Anita Manassakorn

et al.

Cogent Education, Journal Year: 2025, Volume and Issue: 12(1)

Published: Jan. 2, 2025

Early glaucoma detection through accurate optic disc interpretation is essential but challenging for ophthalmology residents. This study evaluated the effectiveness of interactive (ITM) versus non-interactive (NITM) web-based training modules in improving skills diagnosis among Ninety-six residents from five centers Thailand were randomized into ITM or NITM groups. Both groups completed pre- and post-tests containing 30 standardized photographs used self-study with 100 images obtained CLARUS™ 500 over two months. The group received immediate feedback on their answers, while only viewed correct answers without interaction. demonstrated significant improvement scores after (P < 0.001), no difference between = 0.231). Third-year showed greater score compared to first-year 0.009). Satisfaction comparable 0.416). findings suggest that both improve residents' ability evaluate glaucomatous discs, though statistically was found approaches.

Language: Английский

Citations

1
Advancing multiple sclerosis diagnosis through an innovative hybrid AI framework incorporating Multi-view ResNet and quantum RIME-inspired metaheuristics DOI Creative Commons

Mohamed G. Khattap,

Mohammed Sallah, Abdelghani Dahou

et al.

Ain Shams Engineering Journal, Journal Year: 2025, Volume and Issue: 16(2), P. 103241 - 103241

Published: Jan. 13, 2025

Language: Английский

Citations

1
An Explainable Artificial Intelligence Software System for Predicting Diabetes DOI Creative Commons
Parvathaneni Naga Srinivasu, Shakeel Ahmed, M. Hassaballah

et al.

Heliyon, Journal Year: 2024, Volume and Issue: 10(16), P. e36112 - e36112

Published: Aug. 1, 2024

Implementing diabetes surveillance systems is paramount to mitigate the risk of incurring substantial medical expenses. Currently, blood glucose measured by minimally invasive methods, which involve extracting a small sample and transmitting it meter. This method deemed discomforting for individuals who are undergoing it. The present study introduces an Explainable Artificial Intelligence (XAI) system, aims create intelligible machine capable explaining expected outcomes decision models. To this end, we analyze abnormal levels utilizing Bi-directional Long Short-Term Memory (Bi-LSTM) Convolutional Neural Network (CNN). In regard, acquired through oxidase (GOD) strips placed over human body. Later, signal data converted spectrogram images, classified as low glucose, average levels. labeled images then used train individualized monitoring model. proposed XAI model track real-time uses XAI-driven architecture in its feature processing. model's effectiveness evaluated analyzing performance several evolutionary metrics confusion matrix. revealed demonstrate that effectively identifies with elevated

Language: Английский

Citations

7
Prediction of sepsis mortality in ICU patients using machine learning methods DOI Creative Commons
Jiayi Gao, Yu‐Ying Lu,

Negin Ashrafi

et al.

BMC Medical Informatics and Decision Making, Journal Year: 2024, Volume and Issue: 24(1)

Published: Aug. 16, 2024

Abstract Problem Sepsis, a life-threatening condition, accounts for the deaths of millions people worldwide. Accurate prediction sepsis outcomes is crucial effective treatment and management. Previous studies have utilized machine learning prognosis, but limitations in feature sets model interpretability. Aim This study aims to develop that enhances accuracy using reduced set features, thereby addressing previous enhancing Methods analyzes intensive care patient MIMIC-IV database, focusing on adult cases. Employing latest data extraction tools, such as Google BigQuery, following stringent selection criteria, we selected 38 features this study. also informed by comprehensive literature review clinical expertise. Data preprocessing included handling missing values, regrouping categorical variables, Synthetic Minority Over-sampling Technique (SMOTE) balance data. We evaluated several models: Decision Trees, Gradient Boosting, XGBoost, LightGBM, Multilayer Perceptrons (MLP), Support Vector Machines (SVM), Random Forest. The Sequential Halving Classification (SHAC) algorithm was used hyperparameter tuning, both train-test split cross-validation methodologies were employed performance computational efficiency. Results Forest most effective, achieving an area under receiver operating characteristic curve (AUROC) 0.94 with confidence interval ±0.01. significantly outperformed other models new benchmark literature. provided detailed insights into importance various Organ Failure Assessment (SOFA) score average urine output being highly predictive. SHAP (Shapley Additive Explanations) analysis further enhanced model’s interpretability, offering clearer understanding impacts. Conclusion demonstrates significant improvements predicting model, supported advanced techniques thorough preprocessing. Our approach key impacting mortality, making accurate interpretable. By practical utility settings, offer valuable tool healthcare professionals make data-driven decisions, ultimately aiming minimize sepsis-induced fatalities.

Language: Английский

Citations

7
Glaucoma detection with explainable AI using convolutional neural networks based feature extraction and machine learning classifiers DOI Creative Commons
Vijaya Kumar Velpula,

Diksha Sharma,

Lakhan Dev Sharma

et al.

IET Image Processing, Journal Year: 2024, Volume and Issue: 18(13), P. 3827 - 3853

Published: Aug. 19, 2024

Abstract Glaucoma is an eye disease that damages the optic nerve as a result of vision loss, it leading cause blindness worldwide. Due to time‐consuming, inaccurate, and manual nature traditional methods, automation in glaucoma detection important. This paper proposes explainable artificial intelligence (XAI) based model for automatic using pre‐trained convolutional neural networks (PCNNs) machine learning classifiers (MLCs). PCNNs are used feature extractors obtain deep features can capture important visual patterns characteristics from fundus images. Using extracted MLCs then classify healthy An empirical selection CNN MLC parameters has been made performance evaluation. In this work, total 1,865 1,590 images different datasets were used. The results on ACRIMA dataset show accuracy, precision, recall 98.03%, 97.61%, 99%, respectively. Explainable aims create increase user's trust model's decision‐making process transparent interpretable manner. assessment image misclassification carried out facilitate future investigations.

Language: Английский

Citations

7
A three-stage novel framework for efficient and automatic glaucoma classification from retinal fundus images DOI
Law Kumar Singh, Munish Khanna, Hitendra Garg

et al.

Multimedia Tools and Applications, Journal Year: 2024, Volume and Issue: unknown

Published: June 14, 2024

Language: Английский

Citations

6
Cross vision transformer with enhanced Growth Optimizer for breast cancer detection in IoMT environment DOI
Mohamed Abd Elaziz, Abdelghani Dahou, Ahmad O. Aseeri

et al.

Computational Biology and Chemistry, Journal Year: 2024, Volume and Issue: 111, P. 108110 - 108110

Published: May 22, 2024

Language: Английский

Citations

5
Classification of Diabetic Retinopathy Severity Using Deep Learning Techniques on Retinal Images DOI

A. Aruna Kumari,

Avinash Bhagat, Santosh Kumar Henge

et al.

Cybernetics & Systems, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 25

Published: July 13, 2024

Language: Английский

Citations

4
A snake optimization algorithm-based feature selection framework for rapid detection of cardiovascular disease in its early stages DOI
Zahraa Tarek, Amel Ali Alhussan, Doaa Sami Khafaga

et al.

Biomedical Signal Processing and Control, Journal Year: 2024, Volume and Issue: 102, P. 107417 - 107417

Published: Dec. 24, 2024

Language: Английский

Citations

4