Review of Federated Learning and Machine Learning-Based Methods for Medical Image Analysis

Big Data and Cognitive Computing, Journal Year: 2024, Volume and Issue: 8(9), P. 99 - 99

Published: Aug. 28, 2024

Federated learning is an emerging technology that enables the decentralised training of machine learning-based methods for medical image analysis across multiple sites while ensuring privacy. This review paper thoroughly examines federated research applied to analysis, outlining technical contributions. We followed guidelines Okali and Schabram, a methodology, produce comprehensive summary discussion literature in information systems. Searches were conducted at leading indexing platforms: PubMed, IEEE Xplore, Scopus, ACM, Web Science. found total 433 papers selected 118 them further examination. The findings highlighted on applying neural network cardiology, dermatology, gastroenterology, neurology, oncology, respiratory medicine, urology. main challenges reported ability models adapt effectively real-world datasets privacy preservation. outlined two strategies address these challenges: non-independent identically distributed data privacy-enhancing methods. offers reference overview those already working field introduction new topic.

Language: Английский

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Monkeypox diagnosis: improved detection using conditional gans and feature extraction

Journal of Ambient Intelligence and Humanized Computing, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 27, 2025

Language: Английский

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A hybrid long short-term memory-convolutional neural network multi-stream deep learning model with Convolutional Block Attention Module incorporated for monkeypox detection

Science Progress, Journal Year: 2025, Volume and Issue: 108(1)

Published: Jan. 1, 2025

Background Monkeypox (mpox) is a zoonotic infectious disease caused by the mpox virus and characterized painful body lesions, fever, headaches, exhaustion. Since report of first human case in Africa, there have been multiple outbreaks, even nonendemic regions world. The emergence re-emergence highlight critical need for early detection, which has spurred research into applying deep learning to improve diagnostic capabilities. Objective This aims develop robust hybrid long short-term memory (LSTM)-convolutional neural network (CNN) model with Convolutional Block Attention Module (CBAM) provide potential tool detection mpox. Methods A LSTM-CNN multi-stream CBAM was developed trained using Mpox Skin Lesion Dataset Version 2.0 (MSLD v2.0). We employed LSTM layers preliminary feature extraction, CNN further conditioning. evaluated standard metrics, gradient-weighted class activation maps (Grad-CAM) local interpretable model-agnostic explanations (LIME) were used interpretability. Results achieved an F1-score, recall, precision 94%, area under curve 95.04%, accuracy demonstrating competitive performance compared state-of-the-art models. highlights reliability our model. LIME Grad-CAM offered insights model's decision-making process. Conclusion successfully detects mpox, providing promising that can be integrated web mobile platforms convenient widespread use.

Language: Английский

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Enhanced Monkeypox Skin Lesion Classification Using Pooling-Based Vision Transformer Architecture

Advances in computational intelligence and robotics book series, Journal Year: 2025, Volume and Issue: unknown, P. 291 - 306

Published: March 28, 2025

Monkeypox is viral disease transmitted from animals to man and presents symptoms of smallpox especially rashes lesions on the skin. The recent mutation that has led human-to-human transmission caused international concern therefore enhanced method diagnosing required proved. In this part work, we bring forward a powerful approach for monkeypox classification with pooled-based vision transformer mode called as Pooling-based Vision Transformer (PiT) architecture merged MobileNetV3 trained Adam optimizer. By merging strengths both architectures can enhance representation power by integrating local global feature extraction. This hybrid significantly reduces computational load through techniques like token pooling, leading higher accuracy without proportional increase in costs. Lion optimizer employed model convergence response performance contrast other optimizers. For task, proposed was 94.23, 91, 93.5 90.75 % occupancy accuracy, precision, recall, F1 score.

Language: Английский

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From AI to the Era of Explainable AI in Healthcare 5.0: Current State and Future Outlook

Expert Systems, Journal Year: 2025, Volume and Issue: 42(6)

Published: April 29, 2025

ABSTRACT Artificial intelligence (AI) and explainable artificial (XAI) are advancing rapidly, with the potential to deliver significant benefits modern society. The healthcare sector, in particular, has experienced transformative changes; overall, these technologies helping address numerous challenges, such as cancer cell detection, tumour zone identification animal bodies, predictions of major minor diseases, diagnosis, more. This article provides an in‐depth detailed overview AI XAI, focusing on recent trends their implications for Healthcare 5.0 applications. Initially, study examines key concepts exceptional features AI, 5.0. Additional emphasis is placed state‐of‐the‐art practices currently being implemented healthcare, particularly those involving XAI. Subsequently, it establishes a coherent link between XAI 5.0, grounded contemporary advancements. Based findings, algorithms recommended initial obstacles integrating into framework. Proposals further enhancing performance through integration its unique discussed detail. work also implementation strategies highlights model‐specific within frameworks Particular attention given model settings, emphasising contributions improved patient feedback delivery more sophisticated care. Most importantly, this research support sustainable advancements Finally, issues analysed, open discussion presented future guidelines blending

Language: Английский

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Mpox-XDE: an ensemble model utilizing deep CNN and explainable AI for monkeypox detection and classification

BMC Infectious Diseases, Journal Year: 2025, Volume and Issue: 25(1)

Published: March 25, 2025

The daily surge in cases many nations has made the growing number of human monkeypox (Mpox) an important global concern. Therefore, it is imperative to identify Mpox early prevent its spread. majority studies on identification have utilized deep learning (DL) models. However, research developing a reliable method for accurately detecting stages still lacking. This study proposes ensemble model composed three improved DL models more classify phases. We used widely recognized Skin Images Dataset (MSID), which includes 770 images. enhanced Swin Transformer (SwinViT), proposed Mpox-XDE, and modified models-Xception, DenseNet201, EfficientNetB7-were used. To generate model, were combined via Softmax layer, dense flattened 65% dropout. Four neurons final layer dataset into four categories: chickenpox, measles, normal, Mpox. Lastly, average pooling implemented actual class. Mpox-XDE performed exceptionally well, achieving testing accuracy, precision, recall, F1-score 98.70%, 98.90%, 98.80%, respectively. Finally, popular explainable artificial intelligence (XAI) technique, Gradient-weighted Class Activation Mapping (Grad-CAM), was applied convolutional overlaid areas that effectively highlight each illness class dataset. methodology will aid professionals diagnosing patient's condition.

Language: Английский

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Machine Learning-based Intrusion Detection System Through WPA3 Protocol in Smart Contract System

International Journal of Innovative Science and Research Technology (IJISRT), Journal Year: 2024, Volume and Issue: unknown, P. 2926 - 2942

Published: April 17, 2024

Nowadays, the Internet has become one of basic human needs professionals. With massive number devices, reliability, and security will be crucial in coming ages. Routers are common to provide us with internet. These routers can operated different modes. Some use Wifi Security Protocol (WPA) or WPA2, Alliance introduced WPA3 on 25 June 2018. There a lot papers regarding Smart Contract (SC)–based IDS as well Machine Learning-based IDS. Very few discuss combining SC ML-based for authentication processes. In this paper, we how ML plays vital role authentication. Also, play embedded system so that existing vulnerabilities WPA2 reduced 99.62%.

Language: Английский

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Emerging Trends in Applying Artificial Intelligence to Monkeypox Disease: A Bibliometric Analysis

Applied Data Science and Analysis, Journal Year: 2024, Volume and Issue: 2024, P. 148 - 164

Published: Sept. 8, 2024

Monkeypox is a rather rare viral infectious disease that initially did not receive much attention but has recently become subject of concern from the point view public health. Artificial intelligence (AI) techniques are considered beneficial when it comes to diagnosis and identification through medical big data, including imaging other details patients’ information systems. Therefore, this work performs bibliometric analysis incorporate fields AI bibliometrics discuss trends future research opportunities in Monkeypox. A search over various databases was performed title abstracts articles were reviewed, resulting total 251 articles. After eliminating duplicates irrelevant papers, 108 found be suitable for study. In reviewing these studies, given on who contributed topics or fields, what new appeared time, papers most notable. The main added value outline reader process how conduct correct comprehensive by examining real case study related disease. As result, shows great potential improve diagnostics, treatment, health recommendations connected with Possibly, application can enhance responses outcomes since hasten effective interventions.

Language: Английский

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A Short Survey of AI-driven Load Distribution for Electronic Health Record Management with Recent Deployed Techniques, Research Gaps and Challenges

Published: Sept. 18, 2024

Language: Английский

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Performance evaluation of optimal ensemble learning approaches with PCA and LDA-based feature extraction for heart disease prediction

Biomedical Signal Processing and Control, Journal Year: 2024, Volume and Issue: 101, P. 107138 - 107138

Published: Nov. 16, 2024

Language: Английский

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