Medtransnet: advanced gating transformer network for medical image classification

Shaik, Nagur Shareef; Cherukuri, Teja Krishna; Veeranjaneulu, N; Bodapati, Jyostna Devi

doi:10.1007/s00138-024-01542-2

Machine Vision and Applications

2024

DOI: 10.1007/s00138-024-01542-2

|View full text |Cite

Medtransnet: advanced gating transformer network for medical image classification

Nagur Shareef Shaik,

Teja Krishna Cherukuri,

N Veeranjaneulu

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 69 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Transformer Dil-DenseUnet: An Advanced Architecture for Stroke Segmentation

Jazzar,

Mabrouk,

Douik

2024

J. Imaging

View full text Add to dashboard Cite

We propose a novel architecture, Transformer Dil-DenseUNet, designed to address the challenges of accurately segmenting stroke lesions in MRI images. Precise segmentation is essential for diagnosing and treating stroke patients, as it provides critical spatial insights into the affected brain regions and the extent of damage. Traditional manual segmentation is labor-intensive and error-prone, highlighting the need for automated solutions. Our Transformer Dil-DenseUNet combines DenseNet, dilated convolutions, and Transformer blocks, each contributing unique strengths to enhance segmentation accuracy. The DenseNet component captures fine-grained details and global features by leveraging dense connections, improving both precision and feature reuse. The dilated convolutional blocks, placed before each DenseNet module, expand the receptive field, capturing broader contextual information essential for accurate segmentation. Additionally, the Transformer blocks within our architecture address CNN limitations in capturing long-range dependencies by modeling complex spatial relationships through multi-head self-attention mechanisms. We assess our model’s performance on the Ischemic Stroke Lesion Segmentation Challenge 2015 (SISS 2015) and ISLES 2022 datasets. In the testing phase, the model achieves a Dice coefficient of 0.80 ± 0.30 on SISS 2015 and 0.81 ± 0.33 on ISLES 2022, surpassing the current state-of-the-art results on these datasets.

show abstract

Transformer Dil-DenseUnet: An Advanced Architecture for Stroke Segmentation

Jazzar,

Mabrouk,

Douik

2024

J. Imaging

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Medtransnet: advanced gating transformer network for medical image classification

Cited by 1 publication

References 69 publications

Transformer Dil-DenseUnet: An Advanced Architecture for Stroke Segmentation

Transformer Dil-DenseUnet: An Advanced Architecture for Stroke Segmentation

Contact Info

Product

Resources

About