A fuzzy rank-based ensemble of CNN models for MRI segmentation DOI
Daria Valenkova, Asya I. Lyanova, Aleksandr Sinitca

et al.

Biomedical Signal Processing and Control, Journal Year: 2024, Volume and Issue: 102, P. 107342 - 107342

Published: Dec. 28, 2024

Language: Английский

Accessible AI Diagnostics and Lightweight Brain Tumor Detection on Medical Edge Devices DOI Creative Commons
Akmalbek Abdusalomov, Sanjar Mirzakhalilov, Sabina Umirzakova

et al.

Bioengineering, Journal Year: 2025, Volume and Issue: 12(1), P. 62 - 62

Published: Jan. 13, 2025

The timely and accurate detection of brain tumors is crucial for effective medical intervention, especially in resource-constrained settings. This study proposes a lightweight efficient RetinaNet variant tailored edge device deployment. model reduces computational overhead while maintaining high accuracy by replacing the computationally intensive ResNet backbone with MobileNet leveraging depthwise separable convolutions. modified achieves an average precision (AP) 32.1, surpassing state-of-the-art models small tumor (APS: 14.3) large localization (APL: 49.7). Furthermore, significantly costs, making real-time analysis feasible on low-power hardware. Clinical relevance key focus this work. proposed addresses diagnostic challenges small, variable-sized often overlooked existing methods. Its architecture enables portable devices, bridging gap accessibility underserved regions. Extensive experiments BRATS dataset demonstrate robustness across sizes configurations, confidence scores consistently exceeding 81%. advancement holds potential improving early detection, particularly remote areas lacking advanced infrastructure, thereby contributing to better patient outcomes broader AI-driven tools.

Language: Английский

Citations

3
Brain tumor segmentation using multi-scale attention U-Net with EfficientNetB4 encoder for enhanced MRI analysis DOI Creative Commons

R. Preetha,

Jasmine Pemeena Priyadarsini M,

J. S. Nisha

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 22, 2025

Abstract Accurate brain tumor segmentation is critical for clinical diagnosis and treatment planning. This study proposes an advanced framework that combines Multiscale Attention U-Net with the EfficientNetB4 encoder to enhance performance. Unlike conventional U-Net-based architectures, proposed model leverages EfficientNetB4’s compound scaling optimize feature extraction at multiple resolutions while maintaining low computational overhead. Additionally, Multi-Scale Mechanism (utilizing $$1\times 1, 3\times 3$$ , $$5\times 5$$ kernels) enhances representation by capturing boundaries across different scales, addressing limitations of existing CNN-based methods. Our approach effectively suppresses irrelevant regions localization through attention-enhanced skip connections residual attention blocks. Extensive experiments were conducted on publicly available Figshare dataset, comparing EfficientNet variants determine optimal architecture. demonstrated superior performance, achieving Accuracy 99.79%, MCR 0.21%, Dice Coefficient 0.9339, Intersection over Union (IoU) 0.8795, outperforming other in accuracy efficiency. The training process was analyzed using key metrics, including Coefficient, dice loss, precision, recall, specificity, IoU, showing stable convergence generalization. method evaluated against state-of-the-art approaches, surpassing them all accuracy, mean IoU. demonstrates effectiveness robust efficient tumors, positioning it as a valuable tool research applications.

Language: Английский

Citations

0
An Advanced Brain Tumor Detection Model Using a Hybrid (1D/2D) Convolution-Based Efficient Attention Network with Image Feature Extraction DOI

R. Santhi,

Dahlia Sam,

K. R. Nataraj

et al.

Sensing and Imaging, Journal Year: 2025, Volume and Issue: 26(1)

Published: April 16, 2025

Language: Английский

Citations

0
Advancing Breast Cancer Diagnosis: Integrating Deep Transfer Learning and U-Net Segmentation for Precise Classification and Delineation of Ultrasound Images DOI Creative Commons
Divine Senanu Ametefe, Dah John,

Abdulmalik Adozuka Aliu

et al.

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 105047 - 105047

Published: April 1, 2025

Language: Английский

Citations

0
Application of deep learning in magnetic spherule detection: a combined method of YOLOv8 and U-Net models DOI
Zhanfeng Cui, Yonghong Wang

Earth Science Informatics, Journal Year: 2025, Volume and Issue: 18(2)

Published: May 14, 2025

Language: Английский

Citations

0
An End-to-End Approach to Detect Railway Track Defects based on Supervised and Self-Supervised Learning DOI Creative Commons
Muhammad Haroon, Muhammad Jawad Khan, Hammad M. Cheema

et al.

Results in Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 103326 - 103326

Published: Nov. 1, 2024

Language: Английский

Citations

3
Integration of Drone and Machine Learning Technology For Predicting Power Infrastructure Faults Efficiently DOI Creative Commons
WT Al-Shaibani, Ibraheem Shayea, Ramazan Çağlar

et al.

Results in Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 103207 - 103207

Published: Oct. 1, 2024

Language: Английский

Citations

1
A fuzzy rank-based ensemble of CNN models for MRI segmentation DOI
Daria Valenkova, Asya I. Lyanova, Aleksandr Sinitca

et al.

Biomedical Signal Processing and Control, Journal Year: 2024, Volume and Issue: 102, P. 107342 - 107342

Published: Dec. 28, 2024

Language: Английский

Citations

1