Multi-scale conv-attention U-Net for medical image segmentation DOI Creative Commons
Linqiang Pan, Chengxue Zhang, Jingbo Sun

et al.

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

Published: April 8, 2025

U-Net-based network structures are widely used in medical image segmentation. However, effectively capturing multi-scale features and spatial context information of complex organizational remains a challenge. To address this, we propose novel structure based on the U-Net backbone. This model integrates Adaptive Convolution (AC) module, Multi-Scale Learning (MSL) Conv-Attention module to enhance feature expression ability segmentation performance. The AC dynamically adjusts convolutional kernel through an adaptive layer. enables extract different shapes scales adaptively, further improving its performance scenarios. MSL is designed for fusion. It aggregates fine-grained high-level semantic from resolutions, creating rich connections between encoding decoding processes. On other hand, incorporates efficient attention mechanism into skip connections. captures global using low-dimensional proxy high-dimensional data. approach reduces computational complexity while maintaining effective channel extraction. Experimental validation CVC-ClinicDB, MICCAI 2023 Tooth, ISIC2017 datasets demonstrates that our proposed MSCA-UNet significantly improves accuracy robustness. At same time, it lightweight outperforms existing methods.

Language: Английский

Deep Learning in Breast Cancer Imaging: State of the Art and Recent Advancements in Early 2024 DOI Creative Commons
Alessandro Carriero, Léon Groenhoff,

Elizaveta Vologina

et al.

Diagnostics, Journal Year: 2024, Volume and Issue: 14(8), P. 848 - 848

Published: April 19, 2024

The rapid advancement of artificial intelligence (AI) has significantly impacted various aspects healthcare, particularly in the medical imaging field. This review focuses on recent developments application deep learning (DL) techniques to breast cancer imaging. DL models, a subset AI algorithms inspired by human brain architecture, have demonstrated remarkable success analyzing complex images, enhancing diagnostic precision, and streamlining workflows. models been applied diagnosis via mammography, ultrasonography, magnetic resonance Furthermore, DL-based radiomic approaches may play role risk assessment, prognosis prediction, therapeutic response monitoring. Nevertheless, several challenges limited widespread adoption clinical practice, emphasizing importance rigorous validation, interpretability, technical considerations when implementing solutions. By examining fundamental concepts synthesizing latest advancements trends, this narrative aims provide valuable up-to-date insights for radiologists seeking harness power care.

Language: Английский

Citations

31
Skin Cancer Segmentation and Classification Using Vision Transformer for Automatic Analysis in Dermatoscopy-Based Noninvasive Digital System DOI Creative Commons
Galib Muhammad Shahriar Himel, Md. Masudul Islam, Kh. Abdullah Al-Aff

et al.

International Journal of Biomedical Imaging, Journal Year: 2024, Volume and Issue: 2024, P. 1 - 18

Published: Feb. 3, 2024

Skin cancer is a significant health concern worldwide, and early accurate diagnosis plays crucial role in improving patient outcomes. In recent years, deep learning models have shown remarkable success various computer vision tasks, including image classification. this research study, we introduce an approach for skin classification using transformer, state-of-the-art architecture that has demonstrated exceptional performance diverse analysis tasks. The study utilizes the HAM10000 dataset; publicly available dataset comprising 10,015 lesion images classified into two categories: benign (6705 images) malignant (3310 images). This consists of high-resolution captured dermatoscopes carefully annotated by expert dermatologists. Preprocessing techniques, such as normalization augmentation, are applied to enhance robustness generalization model. transformer adapted task. model leverages self-attention mechanism capture intricate spatial dependencies long-range within images, enabling it effectively learn relevant features Segment Anything Model (SAM) employed segment cancerous areas from images; achieving IOU 96.01% Dice coefficient 98.14% then pretrained used architecture. Extensive experiments evaluations conducted assess our approach. results demonstrate superiority over traditional architectures general with some exceptions. Upon experimenting on six different models, ViT-Google, ViT-MAE, ViT-ResNet50, ViT-VAN, ViT-BEiT, ViT-DiT, found out ML achieves 96.15% accuracy Google’s ViT patch-32 low false negative ratio test dataset, showcasing its potential effective tool aiding dermatologists cancer.

Language: Английский

Citations

21
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

32
Automated Classification of Lung Cancer Subtypes Using Deep Learning and CT-Scan Based Radiomic Analysis DOI Creative Commons
Bryce Dunn, Mariaelena Pierobon, Qi Wei

et al.

Bioengineering, Journal Year: 2023, Volume and Issue: 10(6), P. 690 - 690

Published: June 6, 2023

Artificial intelligence and emerging data science techniques are being leveraged to interpret medical image scans. Traditional analysis relies on visual interpretation by a trained radiologist, which is time-consuming can, some degree, be subjective. The development of reliable, automated diagnostic tools key goal radiomics, fast-growing research field combines imaging with personalized medicine. Radiomic studies have demonstrated potential for accurate lung cancer diagnoses prognostications. practice delineating the tumor region interest, known as segmentation, bottleneck in generalized classification models. In this study, incremental multiple resolution residual network (iMRRN), publicly available deep learning segmentation model, was applied automatically segment CT images collected from 355 patients included dataset "Lung-PET-CT-Dx", obtained Cancer Imaging Archive (TCIA), an open-access source radiological images. We report failure rate 4.35% when using iMRRN lesions within plain dataset. Seven algorithms were extracted radiomic features tested their ability classify different subtypes. Over-sampling used handle unbalanced data. Chi-square tests revealed higher order texture most predictive classifying cancers subtype. support vector machine showed highest accuracy, 92.7% (0.97 AUC), three histological subtypes cancer: adenocarcinoma, small cell carcinoma, squamous carcinoma. results demonstrate AI-based computer-aided diagnose coupling supervised classification. Our study integrated application existing AI non-invasive effective diagnosis subtypes, also shed light several practical issues concerning biomedicine.

Language: Английский

Citations

25
A retinal vessel segmentation network with multiple-dimension attention and adaptive feature fusion DOI
Jianyong Li, Ge Gao, Lei Yang

et al.

Computers in Biology and Medicine, Journal Year: 2024, Volume and Issue: 172, P. 108315 - 108315

Published: March 15, 2024

Language: Английский

Citations

13
Harnessing Time-Series Satellite Data and Deep Learning to Monitor Historical Patterns of Deforestation in Eastern Himalayan Foothills of India DOI
Jintu Moni Bhuyan, Subrata Nandy, Hitendra Padalia

et al.

Journal of the Indian Society of Remote Sensing, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 19, 2025

Language: Английский

Citations

1
Machine learning-based morphological and mechanical prediction of kirigami-inspired active composites DOI
Keke Tang, Yujie Xiang, Jie Tian

et al.

International Journal of Mechanical Sciences, Journal Year: 2023, Volume and Issue: 266, P. 108956 - 108956

Published: Dec. 29, 2023

Language: Английский

Citations

21
CFHA-Net: A polyp segmentation method with cross-scale fusion strategy and hybrid attention DOI
Lei Yang,

Chenxu Zhai,

Yanhong Liu

et al.

Computers in Biology and Medicine, Journal Year: 2023, Volume and Issue: 164, P. 107301 - 107301

Published: Aug. 7, 2023

Language: Английский

Citations

18
Automatic reconstruction of closely packed fabric composite RVEs using yarn-level micro-CT images processed by convolutional neural networks (CNNs) and based on physical characteristics DOI
Chongrui Tang, Jianchao Zou, Yifeng Xiong

et al.

Composites Science and Technology, Journal Year: 2024, Volume and Issue: 252, P. 110616 - 110616

Published: April 19, 2024

Language: Английский

Citations

7
Multi-Bottleneck progressive propulsion network for medical image semantic segmentation with integrated macro-micro dual-stage feature enhancement and refinement DOI
Yuefei Wang, Yutong Zhang, Li Zhang

et al.

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 252, P. 124179 - 124179

Published: May 14, 2024

Language: Английский

Citations

7