Role of metaheuristic algorithms in healthcare: a comprehensive investigation across clinical diagnosis, medical imaging, operations management, and public health DOI Creative Commons
Aiman Lameesa, Mahfara Hoque, Md. Sakib Bin Alam

et al.

Journal of Computational Design and Engineering, Journal Year: 2024, Volume and Issue: 11(3), P. 223 - 247

Published: May 1, 2024

Abstract Metaheuristic algorithms have emerged in recent years as effective computational tools for addressing complex optimization problems many areas, including healthcare. These can efficiently search through large solution spaces and locate optimal or near-optimal responses to issues. Although metaheuristic are crucial, previous review studies not thoroughly investigated their applications key healthcare areas such clinical diagnosis monitoring, medical imaging processing, operations management, well public health emergency response. Numerous also failed highlight the common challenges faced by metaheuristics these areas. This thus offers a comprehensive understanding of domains, along with future development. It focuses on specific associated data quality quantity, privacy security, complexity high-dimensional spaces, interpretability. We investigate capacity tackle mitigate efficiently. significantly contributed decision-making optimizing treatment plans resource allocation improving patient outcomes, demonstrated literature. Nevertheless, improper utilization may give rise various complications within medicine despite numerous benefits. Primary concerns comprise employed, challenge ethical considerations concerning confidentiality well-being patients. Advanced optimize scheduling maintenance equipment, minimizing operational downtime ensuring continuous access critical resources.

Language: Английский

Melanoma identification and classification model based on fine-tuned convolutional neural network DOI Creative Commons
Maram Fahaad Almufareh, Noshina Tariq, Mamoona Humayun

et al.

Digital Health, Journal Year: 2024, Volume and Issue: 10

Published: Jan. 1, 2024

Breakthroughs in skin cancer diagnostics have resulted from recent image recognition and Artificial Intelligence (AI) technology advancements. There has been growing that can be lethal to humans. For instance, melanoma is the most unpredictable terrible form of cancer.

Language: Английский

Citations

5
A Comprehensive Survey on Federated Learning in the Healthcare Area: Concept and Applications DOI Open Access

Deepak Upreti,

Eunmok Yang, Hyunil Kim

et al.

Computer Modeling in Engineering & Sciences, Journal Year: 2024, Volume and Issue: 140(3), P. 2239 - 2274

Published: Jan. 1, 2024

Federated learning is an innovative machine technique that deals with centralized data storage issues while maintaining privacy and security.It involves constructing models using datasets spread across several centers, including medical facilities, clinical research Internet of Things devices, even mobile devices.The main goal federated to improve robust benefit from the collective knowledge these disparate without centralizing sensitive information, reducing risk loss, breaches, or exposure.The application in healthcare industry holds significant promise due wealth generated various sources, such as patient records, imaging, wearable surveys.This conducts a systematic evaluation highlights essential for selection implementation approaches healthcare.It evaluates effectiveness strategies field offers analysis domain, encompassing metrics employed.In addition, this study increasing interest applications among scholars provides foundations further studies.

Language: Английский

Citations

5
Combining State-of-the-Art Pre-Trained Deep Learning Models: A Noble Approach for Skin Cancer Detection Using Max Voting Ensemble DOI Creative Commons
Md Hossain, Md. Moazzem Hossain, Most. Binoee Arefin

et al.

Diagnostics, Journal Year: 2023, Volume and Issue: 14(1), P. 89 - 89

Published: Dec. 30, 2023

Skin cancer poses a significant healthcare challenge, requiring precise and prompt diagnosis for effective treatment. While recent advances in deep learning have dramatically improved medical image analysis, including skin classification, ensemble methods offer pathway further enhancing diagnostic accuracy. This study introduces cutting-edge approach employing the Max Voting Ensemble Technique robust classification on ISIC 2018: Task 1-2 dataset. We incorporate range of cutting-edge, pre-trained neural networks, MobileNetV2, AlexNet, VGG16, ResNet50, DenseNet201, DenseNet121, InceptionV3, ResNet50V2, InceptionResNetV2, Xception. These models been extensively trained datasets, achieving individual accuracies ranging from 77.20% to 91.90%. Our method leverages synergistic capabilities these by combining their complementary features elevate performance further. In our approach, input images undergo preprocessing model compatibility. The integrates with architectures weights preserved. For each lesion under examination, every produces prediction. are subsequently aggregated using max voting technique yield final majority-voted class serving as conclusive Through comprehensive testing diverse dataset, outperformed models, attaining an accuracy 93.18% AUC score 0.9320, thus demonstrating superior reliability evaluated effectiveness proposed HAM10000 dataset ensure its generalizability. delivers robust, reliable, tool cancer. By utilizing power advanced we aim assist professionals timely accurate diagnoses, ultimately reducing mortality rates patient outcomes.

Language: Английский

Citations

13
A hierarchical sparrow search algorithm to solve numerical optimization and estimate parameters of carbon fiber drawing process DOI
Jiankai Xue, Bo Shen, Anqi Pan

et al.

Artificial Intelligence Review, Journal Year: 2023, Volume and Issue: 56(S1), P. 1113 - 1148

Published: July 19, 2023

Language: Английский

Citations

11
Role of metaheuristic algorithms in healthcare: a comprehensive investigation across clinical diagnosis, medical imaging, operations management, and public health DOI Creative Commons
Aiman Lameesa, Mahfara Hoque, Md. Sakib Bin Alam

et al.

Journal of Computational Design and Engineering, Journal Year: 2024, Volume and Issue: 11(3), P. 223 - 247

Published: May 1, 2024

Abstract Metaheuristic algorithms have emerged in recent years as effective computational tools for addressing complex optimization problems many areas, including healthcare. These can efficiently search through large solution spaces and locate optimal or near-optimal responses to issues. Although metaheuristic are crucial, previous review studies not thoroughly investigated their applications key healthcare areas such clinical diagnosis monitoring, medical imaging processing, operations management, well public health emergency response. Numerous also failed highlight the common challenges faced by metaheuristics these areas. This thus offers a comprehensive understanding of domains, along with future development. It focuses on specific associated data quality quantity, privacy security, complexity high-dimensional spaces, interpretability. We investigate capacity tackle mitigate efficiently. significantly contributed decision-making optimizing treatment plans resource allocation improving patient outcomes, demonstrated literature. Nevertheless, improper utilization may give rise various complications within medicine despite numerous benefits. Primary concerns comprise employed, challenge ethical considerations concerning confidentiality well-being patients. Advanced optimize scheduling maintenance equipment, minimizing operational downtime ensuring continuous access critical resources.

Language: Английский

Citations

4