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Privacy Enhanced Edge-AI Healthcare Devices Authentication: A Federated Learning Approach

IEEE Transactions on Consumer Electronics 2025年

作者： Ahmed, Syed Thouheed Kaladevi, A.C. Kumar, V. Vinod Shankar, Achyut alqahtani, Fayez REVA University School of Computing and Information Technology India Vellore Institute of Technology School of Computer Science Engineering Vellore632014 India Bennett University School of Computer Science Engineering and Technology Uttar Pradesh Greater Noida201310 India King Saud University College of Computer and Information Sciences Software Engineering Department Riyadh12372 Saudi Arabia

Medical devices connected via internet are open to cyber-attacks, hence the privacy preservation of the user and the devices plays an important role. In this paper, Medical Edge-devices privacy enhanced technique based on the federated learning principles is proposed on the distributed coordination of devices and computational system. The technique generates a dedicated token via Centralized Authentication Authority (CAA) for the active users at the local Federated Learning (FL) cum training model. Further the technique introduces dual layer validation of tokens via Authentication Authority Valuation Layer (AAVL) and Centralized Server Firewall Authentication Layer (CSFal). The purpose of these layers is to optimize the active and inactive tokens, assigning tokens and revoking the inactive tokens from the user groups via distributed federated learning model. The technique is validated on 1500 edge devices of IoT/IoMT on 5 clusters servers of global model. The technique has demonstrated an accuracy of 96% in training and optimizing the IoMT device operations via privacy policies proposed in this paper. © 1975-2011 IEEE.

关键词： Federated learning

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Foundation Models in Computational Pathology: A Review of Challenges, Opportunities, and Impact

arXiv

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arXiv 2025年

作者： Bilal, Mohsin Aadam Raza, Manahil altherwy, Youssef alsuhaibani, Anas Abduljabbar, Abdulrahman almarshad, Fahdah Golding, Paul Rajpoot, Nasir Information Systems Department College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Al-Kharj Saudi Arabia Luddy School of Informatics Computing and Engineering Indiana University Indianapolis United States Tissue Image Analytics Centre Department of Computer Science University of Warwick Coventry United Kingdom Frontier AI San Francisco Bay Area United States

From self-supervised, vision-only models to contrastive visual-language frameworks, computational pathology has rapidly evolved in recent years. Generative AI "co-pilots" now demonstrate the ability to mine subtle, sub-visual tissue cues across the cellular-to-pathology spectrum, generate comprehensive reports, and respond to complex user queries. The scale of data has surged dramatically, growing from tens to millions of multi-gigapixel tissue images, while the number of trainable parameters in these models has risen to several billion. The critical question remains: how will this new wave of generative and multi-purpose AI transform clinical diagnostics? In this article, we explore the true potential of these innovations and their integration into clinical practice. We review the rapid progress of foundation models in pathology, clarify their applications and significance. More precisely, we examine the very definition of foundational models, identifying what makes them foundational, general, or multipurpose, and assess their impact on computational pathology. Additionally, we address the unique challenges associated with their development and evaluation. These models have demonstrated exceptional predictive and generative capabilities, but establishing global benchmarks is crucial to enhancing evaluation standards and fostering their widespread clinical adoption. In computational pathology, the broader impact of frontier AI ultimately depends on widespread adoption and societal acceptance. While direct public exposure is not strictly necessary, it remains a powerful tool for dispelling misconceptions, building trust, and securing regulatory support. © 2025, CC BY-NC-SA.

关键词： Visual languages

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Explainable Anatomical Learning and Deep Generative Models for Tumour Diagnosis

SSRN

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SSRN 2025年

作者： Mansouri, Sofiene alazzam, Malik Bader altowijri, albaraa Ahmad al-Musywel, Haifa A. alhemaidi, Seham Sulieman alsadoun, Ahmed Ahmad, Mohammad Department of Biomedical Technology College of Applied Medical Sciences in Al-Kharj Prince Sattam bin Abdulaziz University Al-Kharj11942 Saudi Arabia Department of Software Engineering Faculty of Information Technology Jadara University Irbid Jordan Department of Surgery College of Medicine University of Tabuk Tabuk Saudi Arabia for the College of Sciences Coordinator of the Training Unit for Medical Physics Students Physics Department Faculty of Sciences Princess Nourah Bint Abdulrahman University Riyadh Saudi Arabia Department of Surgery Division of Ophthalmology University of Tabuk Tabuk Saudi Arabia Department of Medical Surgical Nursing College of Nursing King Saud University Riyadh Saudi Arabia

This study aims to develop an interpretable deep learning model for tumor diagnosis that enhances the accuracy and explainability of anatomical segmentation. Scalar global indices, which are often insensitive to differences in asymmetry or regional configurations, are currently used to quantify changes in tumor and brain area. This is currently accomplished through the use of global indexes, which are currently used. Making an appropriate assessment of pathology-driven anatomical remodelling is one of the most essential phases in the process of diagnosing and treating a wide variety of illnesses. This step is one of the most important steps because it is one of the most important processes. In the last few years, deep learning algorithms have grown gaining popularity for the interpretation of medical images. Despite this popularity, the feature extraction and decision-making procedures of these systems are frequently not interpretable. Within the scope of this investigation, we provide a novel deep learning model that can be interpreted purpose of information analysis. We make use of a deep generative network that is equipped with a hierarchy of conditional latent variables in order to accurately represent a collection of anatomical segmentations. Because of this, we are able to carry out the task with more accuracy. At the very top of this hierarchy is a process in which simultaneous adjustments are made to a two-dimensional latent space in order to differentiate between a variety of clinical situations. A better knowledge of the classification landscape is made possible as a result of this. To further elucidate the classification process, the model's generative properties allow the segmentation space to represent the anatomical variability represented by the discriminative latent space. This is feasible as a result of this situation model may generate new data. © 2025, The Authors. all rights reserved.

关键词： Convolutional neural networks

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Advancing Medical Innovation through Blockchain-Secured Federated Learning for Smart Health

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Khan, Salabat Khan, Mansoor Khan, Muhammad Asghar Wang, Lu Wu, Kaishun Qilu Institute of Technology School of Computer and Information Engineering Jinan China Qilu Institute of Technology School of Intelligent Manufacturing and Control Engineering Jinan China Prince Mohammad Bin Fahd University Department of Electrical Engineering P.O. Box 1664 Al-Khobar31952 Saudi Arabia Shenzhen University College of Computer Science and Software Engineering Shenzhen China Thrust Guangzhou China

The rapid digitization of healthcare systems has led to a vast accumulation of electronic medical records (EMRs), offering an invaluable source of patient data that can significantly advance medical research and improve patient care. However, sharing EMRs for research purposes presents challenges, particularly concerning data privacy, security, and the limitations of traditional centralized data-sharing models. This paper introduces a novel approach that leverages blockchain technology to facilitate federated learning with EMRs, thereby addressing these challenges. Federated learning enables multiple institutions to collaboratively train a robust machine learning model without sharing raw data, preserving privacy and security. By integrating blockchain, this framework enhances data integrity, immutability, and trust, all in a decentralized environment. The blockchain serves as a transparent and secure ledger, recording model updates and aggregating them through a consensus-based mechanism. Smart contracts further enforce data usage policies, allowing only authorized access and maintaining control over data ownership and sharing. This approach empowers medical researchers and institutions to collaborate more effectively, accelerating the discovery of treatments, advancements in personalized medicine, and insights into rare diseases. It also enables patients to contribute to medical research while retaining control over their personal data, fostering a patient-centered approach to healthcare innovation. Experimental results confirm the efficacy and efficiency of this blockchain-enabled federated learning framework, highlighting its potential to transform medical research and adhere to stringent privacy and security standards. This study emphasizes the pivotal role of blockchain in enhancing big data analytics within healthcare, paving the way for improved collaboration, innovation, and patient outcomes. © 2025 IEEE.

关键词： Federated learning

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A Consumer Electronics-Enhanced UAV System for Agricultural Farm Tracking With Fuzzy SMO and Actuator Fault Detection Control algorithms

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IEEE Transactions on Consumer Electronics 2025年

作者： Bilal, Hazrat Aslam, Muhammad Shamrooz Tian, Yibin Ullah, Inam Ayouni, Sarra Vasilakos, Athanasios V. College of Mechatronics and Control Engineering College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China China University of Mining and Technology Artificial Intelligence Research Institute Xuzhou2211106 China Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China Gachon University Department of Computer Engineering Seongnam13120 Korea Republic of Tashkent State University of Economics Department of Artificial Intelligence Tashkent100066 Uzbekistan Princess Nourah bint Abdulrahman University Department of Information Systems College of Computer and Information Sciences Saudi Arabia Grimstad Norway

The adoption of agricultural robots, or agrobots, has revolutionized modern farming operations, ranging from crop monitoring to automated harvesting, significantly boosting productivity. Motivated by the rapid advancements in agrobots and their integration into smart agricultural practices, this study proposes an autonomous trajectory tracking system for wheat farms using quadcopter UAVs. To address actuator fault detection, including stuck faults and partial loss of efficiency, a TSF-H∞-SMO (Takagi-Sugeno Fuzzy-based H∞ Sliding Mode Observer) fault detection framework is introduced. The approach initiates with the derivation of a TSF (Takagi-Sugeno Fuzzy) attitude control model that integrates an uncertainty term, constructed from the original nonlinear dynamics of the UAV and approximated through local linear models at four equilibrium positions. An actuator fault model is subsequently integrated to develop a comprehensive TSF-UAV model, accounting for actuator faults. The TSF-H∞-SMO is then designed using matrix coordinate transformation to enable precise fault detection. The fault detection capabilities of the TSF-H∞-SMO are evaluated through simulations on the TSF-UAV model under SISO (single-input single-output) actuator fault scenarios. The experimental results validate the proposed system, demonstrating its ability to detect a range of actuator faults accurately and promptly. The analysis reveals a proportional relationship between the amplitude of the state change and the severity of the fault, attributed to the interaction between system states and actuator flaps. This approach underscores the potential for deploying autonomous UAV-based fault detection and trajectory tracking systems in agricultural applications. Furthermore, integrating such advanced fault-tolerant control algorithms holds promise for consumer technology applications, where precision, reliability, and robustness are critical to enhancing system performance and operational efficiency. © 1

关键词： Agricultural robots

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Mapping coastal urbanization impacts with object-based image classification and land use/land cover change detection: A focus on sustainable development

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Journal of South American Earth sciences 2025年 161卷

作者： Manikandababu, C.S. alzaben, Nada Maashi, Mashael Geetha, M. Department of Electronics and Communication Engineering Sri Ramakrishna Engineering College Tamil Nadu Coimbatore India Department of Computer Sciences College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P. O. Box 84428 Riyadh 11671 Saudi Arabia Department of Software Engineering College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia S.A.Engineering College Thiruverkadu Tamil Nadu Chennai India

Land use and land cover (LULC) changes are key indicators of environmental and socioeconomic dynamics, influencing biodiversity, climate, water resources, and food security. Understanding these changes is vital for sustainable development and effective land management. This study analyzes LULC transformations in Mexico's Centro Region from 2000 to 2020 using multi-temporal Landsat imagery and the Random Forest (RF) classifier. The integration of remote sensing and machine learning offers accurate, scalable monitoring of land cover dynamics driven by both anthropogenic activities and natural factors. The results reveal significant land cover changes over two decades. Built-up areas expanded markedly, peaking at 14,505.65 *** in 2016, followed by a slight decline. Water bodies declined continuously from 12,648.22 *** in 2000 to 4415.18 *** in 2020. Forest cover showed an overall increase, while shrubland and herbaceous vegetation experienced fluctuations. Cropland increased to 40,447.02 *** by 2020, whereas wetlands underwent a sharp reduction. These findings underscore the dynamic nature of land use patterns and highlight the effectiveness of combining satellite imagery with RF classification for LULC analysis. The study provides valuable insights to support spatial planning, environmental conservation, and sustainable development initiatives. © 2025 Elsevier Ltd

关键词： Land use and land cover (LULC) Landsat imagery Machine learning (ML) Remote sensing Sustainable development Urbanization

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Staging of Different Tumor by Utilizing Laser Guidance (@405 nm) in CT Scan Images along with Statistical Analysis

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Lasers in engineering 2025年第4-6期59卷 309-325页

作者： Amjad, M. Ullah, H. Siddique, M.A. Majid, M.A. aleem, A. Akhtar, M. Gilanie, G. Abdullah-al-Wadud, M. Farhan, M. Akram, A. Batool, Z. Nazir, A. Biophotonics Imaging Techniques Laboratory Institute of Physics The Islamia University of Bahawalpur 63100 Pakistan Department of Computer Science Faculty of Computing The Islamia University of Bahawalpur Bahawalpur Pakistan Dept. of Software Engineering College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia School of Earth Sciences and Engineering Hohai University Nanjing China

Early detection of lung cancer, liver cancer (HCC), and pancreatitis is crucial for effective treatment. This study assessed the use of laser guidance CT scans for diagnosis and staging. 26 patients of CT scan which were collected from BVH and civil hospital Bahawalpur, Pakistan. Visible light with a 405 nm wavelength is used. Results showed staging of lung cancer (2 at stage 1, 2 at stage 2, 3 at stage 4) and HCC (stages 1 to 4a). Pancreatic diseases detected included acute pancreatitis (44%), chronic pancreatitis (22%), gallstones (22%), and pancreatic tumors (11%). alkaline phosphatase, SGOT, and blood urea levels were notably elevated in all three cancers compared to controls. CT scans show promise in identifying cancer stages and diagnosing pancreatitis, aiding in tailored treatment. Statistical analysis shows that alkaline phosphatase emerges as a potential biomarker for these conditions. ©2025 Old City Publishing, Inc.

关键词： Image analysis

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A Hybrid ResNet50-Random Forest Model for Accurate Eye Disease Classification

A Hybrid ResNet50-Random Forest Model for Accurate Eye Disea...

引用

Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Mohit Kumar Goel Gurpreet Singh Jayapal Lande Amaar Haamid Shnaeen Gaurav Sharma Chitkara University Institute of Engineering & Technology Chitkara University Punjab India School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamilnadu India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Early and accurate detection of eye diseases is essential for preventing vision loss. This paper proposes a hybrid model that combines ResNet50 for feature extraction and Random Forest (RF) for classification to improve the automated detection of retinal diseases, including Diabetic Retinopathy, Cataract, and Glaucoma. The dataset used includes 4217 retinal fundus images across four classes: Normal, Diabetic Retinopathy, Cataract, and Glaucoma. The model addresses challenges such as imbalanced datasets and complex decision boundaries. ResNet50 is utilized to extract deep features from the images, while Random Forest efficiently classifies these features with high accuracy. The proposed model achieved strong results, with Precision, Recall, Macro-F1, and Weighted-F1 scores all exceeding 92%, and a low Misclassification Rate of 7.96%. Additionally, explainable AI techniques like Grad-CAM were employed to enhance the model's interpretability, which is vital for its use in clinical settings. The hybrid ResNet50-RF model outperformed standalone models, demonstrating its effectiveness in eye disease classification. This approach offers a promising, reliable solution for automated retinal disease diagnosis and has significant potential for broader application in healthcare.

关键词： Glaucoma Cataracts Radio frequency Diabetic retinopathy Accuracy Retina Feature extraction Reliability Random forests Residual neural networks

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Hand Fracture Detection Using Convolutional Neural Networks: A Deep Learning Approach for Automated Diagnostics

Hand Fracture Detection Using Convolutional Neural Networks:...

引用

Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Mohit Kumar Goel Gurpreet Singh G Sunil Hasssan Muhamedale Gaurav Sharma Chitkara University Institute of Engineering & Technology Chitkara University Punjab India School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamilnadu India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

This paper presents a Convolutional Neural Network (CNN) model developed for the automated identification of hand fractures from X-ray images, aiming to enhance diagnostic accuracy and efficiency in clinical settings. Hand fractures are common injuries that require prompt and precise diagnosis to prevent complications. Traditional diagnostic methods rely on manual interpretation, which can be time-consuming and prone to human error. Leveraging a dataset of 10,000 annotated hand X-ray images, the proposed CNN model was trained and validated using advanced preprocessing techniques and a carefully designed architecture. The model accomplished a training accuracy of 99.45% and a validation accuracy of 96.68% by the 18th epoch, demonstrating its ability to accurately distinguish between fractured and non-fractured hand images. Additionally, the model achieved a training loss of 0.0127 and a validation loss of 0.1408, indicating strong generalization to new data. These results suggest that the CNN model can significantly improve the speed and reliability of hand fracture diagnosis, offering a valuable tool for clinicians, particularly in resource-limited settings. The model's effectiveness highlights the potential of deep learning in medical image analysis and paves the way for its integration into clinical workflows as a decision support system.

关键词： Hands Training Deep learning Adaptation models Technological innovation Accuracy Data models Convolutional neural networks Reliability X-ray imaging

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A Comprehensive CNN Approach for Reliable Breast Cancer Diagnosis

A Comprehensive CNN Approach for Reliable Breast Cancer Diag...

引用

Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Rahul Singh Sheifali Gupta Gotte Ranjith Kumar Haayder M. Abbas Gaurav Sharma Chitkara University Institute of Engineering and Technology Chitkara University Punjab India School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamilnadu India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Breast cancer is a common type of cancer in women worldwide, and early detection is critical for effective treatment. Conventional breast cancer diagnostic methods, such as mammography and biopsy, rely heavily on human interpretation, which can be time-consuming and error-prone. Recently, deep learning methods, specifically Convolutional Neural Networks (CNNs), have shown promise in improving the precision and effectiveness of breast cancer diagnosis. This study describes an innovative Convolutional Neural Network (CNN) method for detecting breast cancer using histopathology images. A total of 8,598 images were collected and divided into two datasets: training (6,860 images) and testing (1,738 images). The CNN structure was explicitly designed for analyzing breast cancer images with Conv2D, MaxPooling2D, Dropout, Flatten, and Dense layers. The model underwent 25 training epochs and was evaluated using loss, accuracy, precision, recall, and F1-Score metrics. A confusion matrix was constructed to assess the model's classification accuracy, and performance metrics were calculated. The CNN model achieved an overall accuracy of 85% when diagnosing breast cancer, demonstrating its effectiveness in automated breast cancer diagnosis.

关键词： Deep learning Training Technological innovation Accuracy Histopathology Predictive models Breast cancer Convolutional neural networks Reliability Testing

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