English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Open

Presents the pdf analysis as premium feature

PDF Analysis

Insights

Presents the summarisation as premium feature

Summarisation

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

The paper presents an automatic brain tumour detection technique in noise corrupted images. The Denoising of the image is implemented using Edge Adaptive Total Variation Denoising Technique (EATVD). The technique is used to preserve the edges in the process of Denoising image. Once the noise is removed from the image, the image is segmented using mean shift clustering. The segmented parts are sent to gray level co-occurrence matrix for feature extraction. The features are used by multi class SVM to detect the tumour in the images. The step followed extracts the tumour with increased precision in noisy images.

Brain tumour detection using mean shift clustering and GLCM features with edge adaptive total variation denoising technique