Comparison of deep learning techniques for prediction of stress distribution in stiffened panels DOI Creative Commons

Narges Mokhtari,

Yuecheng Cai, Jasmin Jelovica

et al.

Thin-Walled Structures, Journal Year: 2025, Volume and Issue: unknown, P. 113494 - 113494

Published: May 1, 2025

Language: Английский

Secure and Decentralized Collaboration in Oncology: A Blockchain Approach to Tumor Segmentation DOI
Ramin Ranjbarzadeh,

Ayse Keles,

Martin Crane

et al.

2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC), Journal Year: 2024, Volume and Issue: unknown, P. 1681 - 1686

Published: July 2, 2024

Language: Английский

Citations

4
Innovative fusion of VGG16, MobileNet, EfficientNet, AlexNet, and ResNet50 for MRI-based brain tumor identification DOI

Marjan Kia,

Soroush Sadeghi,

Homayoun Safarpour

et al.

Iran Journal of Computer Science, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 9, 2024

Language: Английский

Citations

4
Application of Computational Technology in Telehealth and Telesurgery DOI

Sowmya Priyadharshini Subramanian,

Yi Wang

Lecture notes in electrical engineering, Journal Year: 2025, Volume and Issue: unknown, P. 416 - 420

Published: Jan. 1, 2025

Language: Английский

Citations

0
An efficient deep learning strategy for accurate and automated detection of breast tumors in ultrasound image datasets DOI Creative Commons
Luyao Li,

Yupeng Niu,

Fa Tian

et al.

Frontiers in Oncology, Journal Year: 2025, Volume and Issue: 14

Published: March 3, 2025

Background Breast cancer ranks as one of the leading malignant tumors among women worldwide in terms incidence and mortality. Ultrasound examination is a critical method for breast screening diagnosis China. However, conventional ultrasound examinations are time-consuming labor-intensive, necessitating development automated efficient detection models. Methods We developed novel approach based on an improved deep learning model intelligent auxiliary tumors. Combining optimized U2NET-Lite with DeepCardinal-50 model, this demonstrates superior accuracy efficiency precise segmentation classification images compared to traditional models such ResNet AlexNet. Results Our proposed demonstrated exceptional performance experimental test sets. For segmentation, processed 0.9702, recall 0.7961, IoU 0.7063. In classification, excelled, achieving higher AUC values other Specifically, ResNet-50 achieved accuracies 0.78 benign, 0.67 malignant, 0.73 normal cases, while 0.76, 0.63, 0.90 respectively. These results highlight our model’s capability tumor identification classification. Conclusion The automatic benign using can rapidly accurately identify types at early stage, which crucial treatment

Language: Английский

Citations

0
Comparison of deep learning techniques for prediction of stress distribution in stiffened panels DOI Creative Commons

Narges Mokhtari,

Yuecheng Cai, Jasmin Jelovica

et al.

Thin-Walled Structures, Journal Year: 2025, Volume and Issue: unknown, P. 113494 - 113494

Published: May 1, 2025

Language: Английский

Citations

0