Recent Advancements and Future Prospects in Active Deep Learning for Medical Image Segmentation and Classification

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 113623 - 113652

Published: Jan. 1, 2023

Medical images are helpful for the diagnosis, treatment, and evaluation of diseases. Precise medical image segmentation improves diagnosis decision-making, aiding intelligent services better disease management recovery. Due to unique nature images, algorithms based on deep learning face problems such as sample imbalance, edge blur, false positives, negatives. In view these problems, researchers primarily improve network structure but rarely from unstructured aspect. The paper tackles challenges, accentuating limitations convolutional neural network-based methods proposing solutions reduce annotation costs, particularly in complex introduces improvement strategies solve Additionally, article latest learning-based applications analysis, covering segmentation, acquisition, enhancement, registration, classification. Moreover, provides an overview four cutting-edge models, namely (CNN), belief (DBN), stacked autoencoder (SAE), recurrent (RNN). study selection involved searching benchmark academic databases, collecting relevant literature appropriate indicator emphasizing DL-based classification approaches, evaluating performance metrics. research highlights clinicians' scholars' obstacles developing efficient accurate malignancy prognostic framework state-of-the-art deep-learning algorithms. Furthermore, future perspectives explored overcome challenges advance field analysis.

Language: Английский

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Enhancing Prognosis Accuracy for Ischemic Cardiovascular Disease Using K Nearest Neighbor Algorithm: A Robust Approach

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 97879 - 97895

Published: Jan. 1, 2023

Ischemic Cardiovascular diseases are one of the deadliest in world. However, mortality rate can be significantly reduced if we detect disease precisely and effectively. Machine Learning (ML) models offer substantial assistance to individuals requiring early treatment detection realm cardiovascular health. In response this critical need, study developed a robust system predict ischemic accurately using ML-based algorithms. The dataset obtained from Kaggle encompasses comprehensive collection over 918 observations, encompassing 12 essential features crucial for predicting disease. contrast, much-existing research relies primarily on datasets comprising only 303 instances UCI repository. Six algorithms, including K Nearest Neighbors (KNN), Random Forest (RF), Logistic Regression (LR), Support Vector (SVM), Gaussian Naïve Bayes (GNB), Decision Trees (DT), trained heart data. effectiveness proposed methodologies is meticulously evaluated benchmarked against cutting-edge techniques, employing range performance criteria. empirical findings manifest that KNN classifier produced optimized results with 91.8% accuracy, 91.4% recall, 91.9% F1 score, 92.5% precision, AUC 90.27%.

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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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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SPBTGNS: Design of an Efficient Model for Survival Prediction in Brain Tumour Patients using Generative Adversarial Network with Neural Architectural Search Operations

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 140847 - 140869

Published: Jan. 1, 2024

The landscape of medical imaging, particularly in brain tumor analysis and survival prediction, necessitates advancements due to the inherent complexities life-threatening nature tumors. Existing methodologies often struggle with precision efficiency, predominantly limitations handling diverse intricate image datasets. This research presents a novel approach that aims improve accuracy prediction patients tumours, leveraging Generative Adversarial Network (GAN) integrated Neural Architectural Search (NAS) operations. model employs Adaptive Computation Time (ACT) Transformer, method crucial for dynamically adjusting number transformer layers based on complexity input sets. feature is beneficial imaging adapting varying data samples. integration Squeeze-and-Excitation Networks (SENet) enables recalibrate features channel-wise, significantly enhancing sensitivity pivotal MRI images. Furthermore, application Google's AutoML Vision Edge offers efficient neural architecture hyperparameter optimization, specifically tuned Efficient Architecture (ENAS) utilized discover high-performance models lower computational demands, critical aspect where resource constraints are common different use cases. also incorporates customized loss functions, Weighted Cross-Entropy Loss, addressing class imbalance datasets by emphasizing rarer types. Spatial Dropout Batch Normalization as regularization techniques generalization reduce overfitting risks. model's efficacy was validated Br35H, Kaggle Brain Tumor Dataset, IEEE Data Port Dataset Databases, exhibiting notable improvement over existing methods: 5.9% better precision, 6.5% higher accuracy, 4.9% recall analysis. In analysis, demonstrated 8.5% 8.3% among other improvements. These enhancements underscore capability providing more accurate, efficient, reliable predictions patients, potentially revolutionizing diagnosis prognostication clinical settings.

Language: Английский

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Alzheimer’s disease unveiled: Cutting-edge multi-modal neuroimaging and computational methods for enhanced diagnosis

Biomedical Signal Processing and Control, Journal Year: 2024, Volume and Issue: 97, P. 106721 - 106721

Published: Aug. 8, 2024

Language: Английский

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U-Net++DSM: Improved U-Net++ for Brain Tumor Segmentation With Deep Supervision Mechanism

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 132268 - 132285

Published: Jan. 1, 2023

The segmentation of brain tumors is an important and challenging content in medical image processing. Relying solely on human experts to manually segment large volumes data can be time-consuming delay diagnosis. To address this challenge, researchers have set out develop algorithm that automatically determine whether MRI images contain identify their features. This paper proposes the U-Net++DSM, a collaborative approach combining U-Net++ with Deep Supervision Mechanism (DSM) as its backbone. enhance power professionals trained dilation operator using fully annotated images. results method demonstrate combination U-Net++DSM significantly improves accuracy, especially when number fully-labeled limited. show proposed outperforms traditional U-Net models by achieving high performance, surpassing other state-of-the-art models, sensitivity 98.59.00%, specificity 98.72%, accuracy 98.64%, average Dice score 98.81% tested publicly available databases. Compared existing methods, has potential yield even better tumor terms pixel-based classification dice similarity performance metrics.

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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A hybrid approach for multi modal brain tumor segmentation using two phase transfer learning, SSL and a hybrid 3DUNET

Computers & Electrical Engineering, Journal Year: 2024, Volume and Issue: 118, P. 109418 - 109418

Published: July 2, 2024

Language: Английский

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Advancements in deep learning techniques for brain tumor segmentation: A survey

Informatics in Medicine Unlocked, Journal Year: 2024, Volume and Issue: 50, P. 101576 - 101576

Published: Jan. 1, 2024

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