Improved Colorectal Polyp Segmentation Using Enhanced MA-NET and Modified Mix-ViT Transformer DOI Creative Commons
Khaled ELKarazle, Valliappan Raman, Patrick Then

et al.

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 69295 - 69309

Published: Jan. 1, 2023

Colorectal polyps is a prevalent medical condition that could lead to colorectal cancer, leading cause of cancer-related mortality globally, if left undiagnosed. Colonoscopy remains the gold standard for detection and diagnosis neoplasia; however, significant proportion neoplastic lesions are missed during routine examinations, particularly diminutive flat lesions. Deep learning techniques have been employed improve polyp rates in colonoscopy images proven successful reducing miss rate. However, accurate segmentation small major challenge existing models as they struggle differentiate polypoid non-polypoid regions apart. To address this issue, we present an enhanced version Multi-Scale Attention Network (MA-NET) incorporates modified Mix-ViT transformer feature extractor. The facilitates ultra-fine-grained visual categorization accuracy regions. Additionally, introduce pre-processing layer performs histogram equalization on input CIEL*A*B* color space enhance their features. Our model was trained combined dataset comprising Kvasir-SEG CVC-ColonDB cross-validated CVC-ClinicDB ETIS-LaribDB. proposed method demonstrates superior performance compared methods, polyps.

Language: Английский

MTANet: Multi-Task Attention Network for Automatic Medical Image Segmentation and Classification DOI
Yating Ling, Yuling Wang, Wenli Dai

et al.

IEEE Transactions on Medical Imaging, Journal Year: 2023, Volume and Issue: 43(2), P. 674 - 685

Published: Sept. 19, 2023

Medical image segmentation and classification are two of the most key steps in computer-aided clinical diagnosis. The region interest were usually segmented a proper manner to extract useful features for further disease classification. However, these methods computationally complex time-consuming. In this paper, we proposed one-stage multi-task attention network (MTANet) which efficiently classifies objects an while generating high-quality mask each medical object. A reverse addition module was designed task fusion areas global map boundary cues high-resolution features, bottleneck used feature fusion. We evaluated performance MTANet with CNN-based transformer-based architectures across three imaging modalities different tasks: CVC-ClinicDB dataset polyp segmentation, ISIC-2018 skin lesion our private ultrasound liver tumor Our model outperformed state-of-the-art models on all datasets superior 25 radiologists

Language: Английский

Citations

37
Advancing Ocular Imaging: A Hybrid Attention Mechanism-Based U-Net Model for Precise Segmentation of Sub-Retinal Layers in OCT Images DOI Creative Commons
Prakash Kumar Karn, Waleed H. Abdulla

Bioengineering, Journal Year: 2024, Volume and Issue: 11(3), P. 240 - 240

Published: Feb. 28, 2024

This paper presents a novel U-Net model incorporating hybrid attention mechanism for automating the segmentation of sub-retinal layers in Optical Coherence Tomography (OCT) images. OCT is an ophthalmology tool that provides detailed insights into retinal structures. Manual these time-consuming and subjective, calling automated solutions. Our proposed combines edge spatial mechanisms with architecture to improve accuracy. By leveraging mechanisms, focuses selectively on image features. Extensive evaluations using datasets demonstrate our outperforms existing approaches, making it valuable medical professionals. The study also highlights model's robustness through performance metrics such as average Dice score 94.99%, Adjusted Rand Index (ARI) 97.00%, Strength Agreement (SOA) classifications like "Almost Perfect", "Excellent", "Very Strong". advanced predictive shows promise expediting processes enhancing precision ocular imaging real-world applications.

Language: Английский

Citations

9
Medical Image Segmentation Based on Transformer and HarDNet Structures DOI Creative Commons
Tongping Shen, Huanqing Xu

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 16621 - 16630

Published: Jan. 1, 2023

Medical image segmentation is a crucial way to assist doctors in the accurate diagnosis of diseases. However, accuracy medical needs further improvement due problems many noisy images and high similarity between background target regions. The current mainstream networks, such as TransUnet, have achieved segmentation. Still, encoders networks do not consider local connection adjacent chunks lack interaction inter-channel information during upsampling decoder. To address above problems, this paper proposed dual-encoder network, including HarDNet68 Transformer branch, which can extract features global feature input image, allowing network learn more information, thus improving effectiveness In paper, realize fusion different dimensions two stages encoding decoding, we propose adaptation module fuse channel multi-level channels, then improve accuracy. experimental results on CVC-ClinicDB, ETIS-Larib, COVID-19 CT datasets show that model performs better four evaluation metrics, Dice, Iou, Prec, Sens, achieves both internal filling edge prediction images. Accurate making critical cancerous regions advance, ensure cancer patients receive timely targeted treatment, their survival quality.

Language: Английский

Citations

21
DeepPoly: Deep Learning-Based Polyps Segmentation and Classification for Autonomous Colonoscopy Examination DOI Creative Commons
Md Shakhawat Hossain, Md Mahmudur Rahman, M. M. Mahbubul Syeed

et al.

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 95889 - 95902

Published: Jan. 1, 2023

Colorectal cancer (CRC) is the third most common cause of cancer-related deaths in United States and anticipated to another 52,580 2023. The standard medical procedure for screening treating colorectal disease a colonoscopy. By effectively examining colonoscopy identify precancerous polyps early remove them before they become cancerous, CRC mortality can be lowered significantly. Manual examination detection time-consuming, tedious, prone human error. Automatic segmentation could fast practical; however, existing automated methods fail attain adequate accuracy segmentation. Moreover, these do not assess risk detected polyps. In this paper, we proposed an autonomous method detect their potential threats. utilized DoubleU-Net Vision Transformer (ViT) classifying based on risks. has achieved mean dice-coefficient 0.834 0.956 Endotech challenge Kvasir-SEG dataset, accordingly, outperforming state-of-the-art Then, classified segmented as hyper-plastic or adenomatous with 99% test accuracy.

Language: Английский

Citations

18
MGCBFormer: The multiscale grid-prior and class-inter boundary-aware transformer for polyp segmentation DOI
Yang Xia,

Haijiao Yun,

Yanjun Liu

et al.

Computers in Biology and Medicine, Journal Year: 2023, Volume and Issue: 167, P. 107600 - 107600

Published: Oct. 20, 2023

Language: Английский

Citations

14
A new segmentation algorithm for peripapillary atrophy and optic disk from ultra-widefield Photographs DOI
Cheng Wan, Jiyi Fang,

Kunke Li

et al.

Computers in Biology and Medicine, Journal Year: 2024, Volume and Issue: 172, P. 108281 - 108281

Published: March 13, 2024

Language: Английский

Citations

5
BMCS-Net: A Bi-directional multi-scale cascaded segmentation network based on transformer-guided feature Aggregation for medical images DOI
Bicao Li, Jing Wang, Bei Wang

et al.

Computers in Biology and Medicine, Journal Year: 2024, Volume and Issue: 180, P. 108939 - 108939

Published: July 29, 2024

Language: Английский

Citations

5
Improving Skin Lesion Segmentation with Self-Training DOI Open Access
Aleksandra Dzieniszewska, Piotr Garbat, Ryszard Piramidowicz

et al.

Cancers, Journal Year: 2024, Volume and Issue: 16(6), P. 1120 - 1120

Published: March 11, 2024

Skin lesion segmentation plays a key role in the diagnosis of skin cancer; it can be component both traditional algorithms and end-to-end approaches. The quality directly impacts accuracy classification; however, attaining optimal necessitates substantial amount labeled data. Semi-supervised learning allows for employing unlabeled data to enhance results machine model. In case medical image segmentation, acquiring detailed annotation is time-consuming costly requires skilled individuals so utilization significant mitigation manual efforts. This study proposes novel approach semi-supervised using self-training with Noisy Student. utilizing large amounts available images. It consists four steps—first, training teacher model on only, then generating pseudo-labels model, student pseudo-labeled data, lastly, student* generated this work, we implemented DeepLabV3 architecture as models. As final result, achieved mIoU 88.0% ISIC 2018 dataset 87.54% PH2 dataset. evaluation proposed shows that Student improves performance neural networks task while only small

Language: Английский

Citations

4
Polyp-Mamba: Polyp Segmentation with Visual Mamba DOI
Zhongxing Xu, Feilong Tang, Zhe Chen

et al.

Lecture notes in computer science, Journal Year: 2024, Volume and Issue: unknown, P. 510 - 521

Published: Jan. 1, 2024

Language: Английский

Citations

4
Masked hybrid attention with Laplacian query fusion and tripartite sequence matching for medical image segmentation DOI Creative Commons
Favour Ekong, Yongbin Yu, Rutherford Agbeshi Patamia

et al.

Neural Computing and Applications, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 6, 2025

Language: Английский

Citations

0