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/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Tumour segmentation is a crucial step in automated analysis of thermal images for early detection of breast cancer. Existing algorithms use clustering, thresholding and active contour techniques that rely on low-level image features generated from manual feature engineering. However, recent advances in deep learning, especially Convolutional Neural Networks (CNNs), are slowly making them the de-facto method for automated image analysis. In this paper, we explore various CNN architectures for semantic segmentation starting from naive patch based classifiers to more sophisticated ones including several variations of the encoder-decoder architecture for detecting the hotspots in the thermal image. We also show that encoder decoder architectures perform better when compared to patch based classifiers in terms of accuracy, dice index, jaccard index and inference time even with small thermal image datasets.

Exploring Deep Learning Networks for Tumour Segmentation in Infrared Images