Improved Prostate Tumor Segmentation Using Self-Attention Mechanism and SAM Model DOI

Pratiksh Kumar,

P. K. Sahu,

Shubhendra Gautam

et al.

Published: Dec. 6, 2024

Language: Английский

SASU-Net:Hyperspectral video tracker based on spectral adaptive aggregation weighting and scale updating DOI
Dong Zhao,

H. B. Zhang,

Kunpeng Huang

et al.

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 126721 - 126721

Published: Feb. 1, 2025

Language: Английский

Citations

2
Advanced deep learning and large language models: Comprehensive insights for cancer detection DOI
Yassine Habchi, Hamza Kheddar, Yassine Himeur

et al.

Image and Vision Computing, Journal Year: 2025, Volume and Issue: unknown, P. 105495 - 105495

Published: March 1, 2025

Language: Английский

Citations

0
Cancer diagnosis in smart healthcare: Optimization of the MamCancerX model’s multiple instance learning framework DOI

Yuliang Gai,

Ji Hao,

Yuxin Liu

et al.

Alexandria Engineering Journal, Journal Year: 2025, Volume and Issue: 125, P. 566 - 574

Published: April 23, 2025

Language: Английский

Citations

0
High precision banana variety identification using vision transformer based feature extraction and support vector machine DOI Creative Commons
Ebru Ergün

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

Published: March 26, 2025

Bananas, renowned for their delightful flavor, exceptional nutritional value, and digestibility, are among the most widely consumed fruits globally. The advent of advanced image processing, computer vision, deep learning (DL) techniques has revolutionized agricultural diagnostics, offering innovative automated solutions detecting classifying fruit varieties. Despite significant progress in DL, accurate classification banana varieties remains challenging, particularly due to difficulty identifying subtle features at early developmental stages. To address these challenges, this study presents a novel hybrid framework that integrates Vision Transformer (ViT) model global semantic feature representation with robust capabilities Support Vector Machines. proposed was rigorously evaluated on two datasets: four-class BananaImageBD six-class BananaSet. mitigate data imbalance issues, evaluation strategy employed, resulting remarkable accuracy rate (CAR) 99.86% $$\:\pm\:$$ 0.099 BananaSet 99.70% 0.17 BananaImageBD, surpassing traditional methods by margin 1.77%. ViT model, leveraging self-supervised semi-supervised mechanisms, demonstrated promise extracting nuanced critical applications. By combining cutting-edge machine classifiers, system establishes new benchmark precision reliability detection These findings underscore potential DL frameworks advancing diagnostics pave way future innovations domain.

Language: Английский

Citations

0
Integrated Violence and Weapon Detection Using Deep Learning DOI
Vaibhav Yadav,

Sanskar Kumar,

Atul Goyal

et al.

Published: Aug. 2, 2024

Language: Английский

Citations

0
A semi-supervised approach for breast tumor segmentation using sparse transformer attention UNet DOI
Muhammad Wajid, Ahmed Iqbal,

Isra Malik

et al.

Pattern Recognition Letters, Journal Year: 2024, Volume and Issue: 187, P. 63 - 72

Published: Nov. 10, 2024

Language: Английский

Citations

0
Improved Prostate Tumor Segmentation Using Self-Attention Mechanism and SAM Model DOI

Pratiksh Kumar,

P. K. Sahu,

Shubhendra Gautam

et al.

Published: Dec. 6, 2024

Language: Английский

Citations

0