Digital Frontiers in Healthcare: Integrating mHealth, AI, and Radiology for Future Medical Diagnostics

Biomedical engineering, Journal Year: 2024, Volume and Issue: unknown

Published: Jan. 23, 2024

In an era where technology and healthcare increasingly intertwine, we stand on the precipice of a seismic shift in future medicine. This chapter unravels confluence mHealth, artificial intelligence (AI), radiology as it navigates labyrinth these advancements, dissecting their unique qualities, historical evolution, projected trajectories. From democratising potential mHealth to AI’s analytical might, transformative journey medical imaging from film digital—the offers deep dive into current realities horizons. Further, intersection domains is explored, illuminating revolutionary role enhancing capabilities through advances imaging. An exhaustive review cutting-edge applications ethico-regulatory conundrums they pose, forms substantial part discourse, followed by foresight anticipated technological breakthroughs, impacts, critical policymakers health leaders this odyssey. The culminates holistic synthesis, tying together strands preceding sections underscore triumvirate. text designed captivating exploration, reflective critique, roadmap for collectively navigate towards technologically empowered era.

Language: Английский

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Transforming educational insights: strategic integration of federated learning for enhanced prediction of student learning outcomes

The Journal of Supercomputing, Journal Year: 2024, Volume and Issue: 80(11), P. 16334 - 16367

Published: April 10, 2024

Language: Английский

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Redefining brain tumor segmentation: a cutting-edge convolutional neural networks-transfer learning approach

International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering, Journal Year: 2024, Volume and Issue: 14(3), P. 2583 - 2583

Published: April 4, 2024

Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, ability to precisely delineate boundaries from magnetic resonance imaging (MRI) scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) transfer learning, integrating state-of-the-art Deeplabv3+ architecture ResNet18 backbone. The model is rigorously trained and evaluated, exhibiting remarkable performance metrics, including impressive global accuracy 99.286%, a high-class 82.191%, mean intersection over union (IoU) 79.900%, weighted IoU 98.620%, Boundary F1 (BF) score 83.303%. Notably, detailed comparative existing methods showcases superiority our proposed model. These findings underscore model’s competence in precise localization, underscoring its potential revolutionize medical enhance healthcare outcomes. research paves way future exploration optimization advanced CNN models imaging, emphasizing addressing false positives resource efficiency.

Language: Английский

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Investigating Deep Learning for Early Detection and Decision-Making in Alzheimer’s Disease: A Comprehensive Review

Neural Processing Letters, Journal Year: 2024, Volume and Issue: 56(3)

Published: April 24, 2024

Abstract Alzheimer’s disease (AD) is a neurodegenerative disorder that affects millions of people worldwide, making early detection essential for effective intervention. This review paper provides comprehensive analysis the use deep learning techniques, specifically convolutional neural networks (CNN) and vision transformers (ViT), classification AD using brain imaging data. While previous reviews have covered similar topics, this offers unique perspective by providing detailed comparison CNN ViT classification, highlighting strengths limitations each approach. Additionally, presents an updated thorough most recent studies in field, including latest advancements architectures, training methods, performance evaluation metrics. Furthermore, discusses ethical considerations challenges associated with models such as need interpretability potential bias. By addressing these issues, aims to provide valuable insights future research clinical applications, ultimately advancing field techniques.

Language: Английский

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Deep Transfer Learning-Based Automated Diabetic Retinopathy Detection Using Retinal Fundus Images in Remote Areas

International Journal of Computational Intelligence Systems, Journal Year: 2024, Volume and Issue: 17(1)

Published: May 29, 2024

Abstract Diabetic retinopathy (DR) significantly burdens ophthalmic healthcare due to its wide prevalence and high diagnostic costs. Especially in remote areas with limited medical access, undetected DR cases are on the rise. Our study introduces an advanced deep transfer learning-based system for real-time detection using fundus cameras address this. This research aims develop efficient timely assistance patients, empowering them manage their health better. The proposed leverages imaging collect retinal images, which then transmitted processing unit effective disease severity classification. Comprehensive reports guide subsequent actions based identified stage. achieves by utilizing learning algorithms, specifically VGGNet. system’s performance is rigorously evaluated, comparing classification accuracy previous outcomes. experimental results demonstrate robustness of system, achieving impressive 97.6% during phase, surpassing existing approaches. Implementing automated has transformed dynamics, enabling early, cost-effective diagnosis millions. also streamlines patient prioritization, facilitating interventions early-stage cases.

Language: Английский

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A Novel Heteromorphous Convolutional Neural Network for Automated Assessment of Tumors in Colon and Lung Histopathology Images

Biomimetics, Journal Year: 2023, Volume and Issue: 8(4), P. 370 - 370

Published: Aug. 16, 2023

The automated assessment of tumors in medical image analysis encounters challenges due to the resemblance colon and lung non-mitotic nuclei their heteromorphic characteristics. An accurate tumor presence is crucial for determining aggressiveness grading. This paper proposes a new method called ColonNet, heteromorphous convolutional neural network (CNN) with feature grafting methodology categorically configured analyzing mitotic histopathology images. ColonNet model consists two stages: first, identifying potential patches within histopathological imaging areas, second, categorizing these into squamous cell carcinomas, adenocarcinomas (lung), benign (colon), (colon) based on model’s guidelines. We develop employ our deep CNNs, each capturing distinct structural, textural, morphological properties nuclei, construct CNN. execution proposed analyzed by its comparison state-of-the-art CNNs. results demonstrate that surpasses others test set, achieving an impressive F1 score 0.96, sensitivity specificity 0.95, area under accuracy curve 0.95. These outcomes underscore hybrid superior performance, excellent generalization, accuracy, highlighting as valuable tool support pathologists diagnostic activities.

Language: Английский

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Melanoma identification and classification model based on fine-tuned convolutional neural network

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

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In-depth review of AI-enabled unmanned aerial vehicles: trends, vision, and challenges

Discover Artificial Intelligence, Journal Year: 2024, Volume and Issue: 4(1)

Published: Dec. 2, 2024

In recent times, AI and UAV have progressed significantly in several applications. This article analyzes applications of with modern green computing various sectors. It addresses cutting-edge technologies such as computing, generative AI, future scope, related concerns UAV. The research investigates the role combination UAVs for navigation, object recognition tracking, wildlife monitoring, precision agriculture, rescue operations, surveillance, communication. study examines how are being applied disaster management, other areas. ethics applications, including safety, legal frameworks, issues, thoroughly investigated. AI-based across different disciplines, using open-source data current advancements growth this domain. investigation will aid researchers their exploration technologies.

Language: Английский

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Combining State-of-the-Art Pre-Trained Deep Learning Models: A Noble Approach for Skin Cancer Detection Using Max Voting Ensemble

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

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An Optimized Role-Based Access Control Using Trust Mechanism in E-Health Cloud Environment

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 138813 - 138826

Published: Jan. 1, 2023

In today’s world, services are improved and advanced in every field of life. Especially the health sector, information technology (IT) plays a vigorous role electronic (e-health). To achieve benefits from e-health, its cloud-based implementation is necessary. With this environment’s multiple benefits, privacy security loopholes exist. As number users grows, Electronic Healthcare System’s (EHS) response time becomes slower. This study presented trust mechanism for access control (AC) known as role-based (RBAC) to address issue. method observes user’s behavior assigns roles based on it. The AC module has been implemented using SQL Server, an administrator develops controls with EHS modules. validate value, A .net-based framework introduced. e-health proposed research ensures that can protect their data intruders other threats.

Language: Английский

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