Artificial intelligence based medical decision support system for early and accurate breast cancer prediction

Advances in Engineering Software, Journal Year: 2022, Volume and Issue: 175, P. 103338 - 103338

Published: Nov. 19, 2022

Language: Английский

---

Emperor penguin optimization algorithm- and bacterial foraging optimization algorithm-based novel feature selection approach for glaucoma classification from fundus images

Soft Computing, Journal Year: 2023, Volume and Issue: 28(3), P. 2431 - 2467

Published: May 27, 2023

Language: Английский

---

A novel enhanced hybrid clinical decision support system for accurate breast cancer prediction

Measurement, Journal Year: 2023, Volume and Issue: 221, P. 113525 - 113525

Published: Sept. 3, 2023

Language: Английский

---

Bio-Inspired Feature Selection Algorithms With Their Applications: A Systematic Literature Review

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 43733 - 43758

Published: Jan. 1, 2023

Based on the principles of biological evolution nature, bio-inspired algorithms are gaining popularity in developing robust techniques for optimization. Unlike gradient descent optimization methods, these metaheuristic computationally less expensive, and can also considerably perform well with nonlinear high-dimensional data. Objectives: To understand algorithms, application domains, effectiveness, challenges feature selection techniques. Method: A systematic literature review is conducted five major digital databases science engineering. Results: The primary search included 695 articles. After removing 263 duplicated articles, 432 studies remained to be screened. Among those, 317 irrelevant papers were removed. We then excluded 77 according exclusion criteria. Finally, 38 articles selected this study. Conclusion: Out studies, 28 discussed Swarm-based 2 studied Genetic Algorithms, 8 covered both categories. Considering 21 focused problems healthcare sector, while rest mainly investigated issues cybersecurity, text classification, image processing. Hybridization other BIAs was employed by approximately 18.5% papers, 13 out used S-shaped transfer functions. majority supervised classification methods such as k-NN SVM building fitness Accordingly, we conclude that future research should focus applying a diverse area applications finance social networks. And further exploration into enhancement quantum representation, rough set theory, chaotic maps, Lévy flight necessary. Additionally, suggest investigating functions besides S-shaped, V-shaped X-shaped. Moreover, clustering deep learning models constructing need further.

Language: Английский

---

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

---

Nature-Inspired Algorithms-Based Optimal Features Selection Strategy for COVID-19 Detection Using Medical Images

New Generation Computing, Journal Year: 2024, Volume and Issue: 42(4), P. 761 - 824

Published: May 10, 2024

Language: Английский

---

A concentrated machine learning-based classification system for age-related macular degeneration (AMD) diagnosis using fundus images

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Jan. 29, 2024

Abstract The increase in eye disorders among older individuals has raised concerns, necessitating early detection through regular examinations. Age-related macular degeneration (AMD), a prevalent condition over 45, is leading cause of vision impairment the elderly. This paper presents comprehensive computer-aided diagnosis (CAD) framework to categorize fundus images into geographic atrophy (GA), intermediate AMD, normal, and wet AMD categories. crucial for precise age-related enabling timely intervention personalized treatment strategies. We have developed novel system that extracts both local global appearance markers from images. These are obtained entire retina iso-regions aligned with optical disc. Applying weighted majority voting on best classifiers improves performance, resulting an accuracy 96.85%, sensitivity 93.72%, specificity 97.89%, precision 93.86%, F1 ROC 95.85%, balanced 95.81%, sum 95.38%. not only achieves high but also provides detailed assessment severity each retinal region. approach ensures final aligns physician’s understanding aiding them ongoing follow-up patients.

Language: Английский

---

Enhancing foveal avascular zone analysis for Alzheimer’s diagnosis with AI segmentation and machine learning using multiple radiomic features

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Jan. 22, 2024

Abstract We propose a hybrid technique that employs artificial intelligence (AI)-based segmentation and machine learning classification using multiple features extracted from the foveal avascular zone (FAZ)—a retinal biomarker for Alzheimer’s disease—to improve disease diagnostic performance. Imaging data of optical coherence tomography angiography 37 patients with 48 healthy controls were investigated. The presence or absence brain amyloids was confirmed amyloid positron emission tomography. In superficial capillary plexus scans, FAZ automatically segmented an AI method to extract biomarkers (area, solidity, compactness, roundness, eccentricity), which paired clinical (age sex) as common correction variables. used light-gradient boosting (a is algorithm based on trees utilizing gradient boosting) diagnose by integrating corresponding radiomic biomarkers. Fivefold cross-validation applied analysis, performance determined area under curve. proposed achieved curve $$72.2\pm 4.2$$ 72.2 ± 4.2 %, outperforming existing single-feature (area) criteria over 13%. Furthermore, in holdout test set, exhibited 14% improvement compared single features, achieving 72.0± 4.8%. Based these facts, we have demonstrated effectiveness our technology significant improvements FAZ-based diagnosis research through use eccentricity).

Language: Английский

---

Improved Kepler Optimization Algorithm for enhanced feature selection in liver disease classification

Knowledge-Based Systems, Journal Year: 2024, Volume and Issue: 297, P. 111960 - 111960

Published: May 16, 2024

Language: Английский

---

Glaucoma detection with explainable AI using convolutional neural networks based feature extraction and machine learning classifiers

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

---