Image super‐resolution via dynamic network DOI Creative Commons

Chunwei Tian,

Xuanyu Zhang, Qi Zhang

et al.

CAAI Transactions on Intelligence Technology, Journal Year: 2024, Volume and Issue: 9(4), P. 837 - 849

Published: April 8, 2024

Abstract Convolutional neural networks depend on deep network architectures to extract accurate information for image super‐resolution. However, obtained of these convolutional cannot completely express predicted high‐quality images complex scenes. A dynamic super‐resolution (DSRNet) is presented, which contains a residual enhancement block, wide feature refinement block and construction block. The composed enhanced architecture facilitate hierarchical features To enhance robustness model scenes, achieves learn more robust applicability an varying prevent interference components in utilises stacked accurately features. Also, learning operation embedded the long‐term dependency problem. Finally, responsible reconstructing images. Designed heterogeneous can not only richer structural information, but also be lightweight, suitable mobile digital devices. Experimental results show that our method competitive terms performance, recovering time complexity. code DSRNet at https://github.com/hellloxiaotian/DSRNet .

Language: Английский

Deep learning approach for brain tumor classification using metaheuristic optimization with gene expression data DOI

Amol Avinash Joshi,

Rabia Musheer Aziz

International Journal of Imaging Systems and Technology, Journal Year: 2023, Volume and Issue: 34(2)

Published: Dec. 16, 2023

Abstract This study addresses the critical challenge of accurately classifying brain tumors using artificial intelligence. Early detection is crucial, as untreated can be fatal. Despite advances in AI, remains a challenging task. To address this challenge, we propose novel optimization approach called PSCS combined with deep learning for tumor classification. optimizes classification process by improving Particle Swarm Optimization (PSO) exploitation Cuckoo search (CS) algorithm. Next, classified gene expression data Deep Learning (DL) to identify different groups or classes related particular along technique. The proposed technique DL achieves much better accuracy than other existing and Machine models evaluation matrices such Recall, Precision, F1‐Score, confusion matrix. research contributes AI‐driven diagnosis classification, offering promising solution improved patient outcomes.

Language: Английский

Citations

33
Advance brain tumor segmentation using feature fusion methods with deep U-Net model with CNN for MRI data DOI Creative Commons
Abdul Haseeb Nizamani, Zhigang Chen, Ahsan Ahmed Nizamani

et al.

Journal of King Saud University - Computer and Information Sciences, Journal Year: 2023, Volume and Issue: 35(9), P. 101793 - 101793

Published: Oct. 1, 2023

In modern healthcare, the precision of medical image segmentation holds immense significance for diagnosis and treatment planning. Deep learning techniques, such as CNNs, UNETs, Transformers, have revolutionized this field by automating previously labor-intensive manual processes. However, challenges like intricate structures indistinct features persist, leading to accuracy issues. Researchers are diligently addressing these further unlock potential in healthcare transformation. To enhance brain tumor MRI segmentation, our study introduces three novel feature-enhanced hybrid UNet models (FE-HU-NET): FE1-HU-NET, FE2-HU-NET, FE3-HU-NET. Our approach encompasses main aspects. Initially, we emphasize feature enhancement during preprocessing stage. We apply distinct techniques—CLAHE, MHE, MBOBHE—to each model. Secondly, tailor architecture model results, focusing on a personalized layered design. Lastly, employ CNN post-processing refine outcomes through additional convolutional layers. The HU-Net module, shared across models, integrates customized layer CNN. also introduce an alternative variant, FE4-HU-NET, utilizing DeepLABv3 Incorporating CLAHE bolstered layers, variant offers approach. Rigorous experimentation underscores excellence proposed framework distinguishing complex tissues, surpassing current state-of-the-art models. Impressively, achieve rates exceeding 99% two publicly available datasets. Performance metrics Jaccard index, sensitivity, specificity substantiate effectiveness Hybrid U-Net

Language: Английский

Citations

30
Fully automated diagnosis of thyroid nodule ultrasound using brain-inspired inference DOI
Guanghui Li, Qinghua Huang, Chunying Liu

et al.

Neurocomputing, Journal Year: 2024, Volume and Issue: 582, P. 127497 - 127497

Published: March 7, 2024

Language: Английский

Citations

15
Early detection of brain tumors: Harnessing the power of GRU networks and hybrid dwarf mongoose optimization algorithm DOI

Yang Yuxia,

Chaoluomeng,

Navid Razmjooy

et al.

Biomedical Signal Processing and Control, Journal Year: 2024, Volume and Issue: 91, P. 106093 - 106093

Published: Feb. 7, 2024

Language: Английский

Citations

14
A deep learning fusion model for accurate classification of brain tumours in Magnetic Resonance images DOI Creative Commons
Nechirvan Asaad Zebari, Chira N. Mohammed, Dilovan Asaad Zebari

et al.

CAAI Transactions on Intelligence Technology, Journal Year: 2024, Volume and Issue: 9(4), P. 790 - 804

Published: Jan. 4, 2024

Abstract Detecting brain tumours is complex due to the natural variation in their location, shape, and intensity images. While having accurate detection segmentation of would be beneficial, current methods still need solve this problem despite numerous available approaches. Precise analysis Magnetic Resonance Imaging (MRI) crucial for detecting, segmenting, classifying medical diagnostics. a vital component diagnosis, it requires precise, efficient, careful, reliable image techniques. The authors developed Deep Learning (DL) fusion model classify reliably. models require large amounts training data achieve good results, so researchers utilised augmentation techniques increase dataset size models. VGG16, ResNet50, convolutional deep belief networks extracted features from MRI Softmax was used as classifier, set supplemented with intentionally created images addition genuine ones. two DL were combined proposed generate model, which significantly increased classification accuracy. An openly accessible internet test model's performance, experimental results showed that achieved accuracy 98.98%. Finally, compared existing methods, outperformed them significantly.

Language: Английский

Citations

13
Green and fast prediction of crude protein contents in bee pollen based on digital images combined with Random Forest algorithm DOI

Leandra Schuastz Breda,

José Elton de Melo Nascimento, Vandressa Alves

et al.

Food Research International, Journal Year: 2024, Volume and Issue: 179, P. 113958 - 113958

Published: Jan. 10, 2024

Language: Английский

Citations

12
Advancing ASD detection: novel approach integrating attention graph neural networks and crossover boosted meerkat optimization DOI
Lipika Goel, Sonam Gupta, Avdhesh Gupta

et al.

International Journal of Machine Learning and Cybernetics, Journal Year: 2024, Volume and Issue: 15(8), P. 3279 - 3297

Published: Feb. 8, 2024

Language: Английский

Citations

11
Recent advances of artificial intelligence in quantitative analysis of food quality and safety indicators: a review DOI
Lunzhao Yi, Wenfu Wang,

Yuhua Diao

et al.

TrAC Trends in Analytical Chemistry, Journal Year: 2024, Volume and Issue: 180, P. 117944 - 117944

Published: Aug. 29, 2024

Language: Английский

Citations

9
An automatic classification framework for identifying type of plant leaf diseases using multi-scale feature fusion-based adaptive deep network DOI

Bathula Nagachandrika,

R. Prasath,

Praveen Joe I R

et al.

Biomedical Signal Processing and Control, Journal Year: 2024, Volume and Issue: 95, P. 106316 - 106316

Published: April 26, 2024

Language: Английский

Citations

8
Deep-learning-based aesthetic evaluation network for bridge pylon and aesthetics-oriented bridge design DOI
Xiang Cheng, Airong Chen, Dalei Wang

et al.

Structures, Journal Year: 2025, Volume and Issue: 71, P. 108167 - 108167

Published: Jan. 1, 2025

Language: Английский

Citations

1