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 Tools annual

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 monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

In this study, a morphological differentiation-based method has beenintroduced which employs temperature distribution on the tissue surface todetect brain tumor's malignancy. According to the common tumor CT scans, twodifferent scenarios have been implemented to describe irregular shape of themalignant tumor. In the first scenario, tumor has been considered as a polygonbase prism and in the second one, it has been considered as a star-shaped baseprism. By increasing the number of sides of the polygon or wings of the star,degree of the malignancy has been increased. Constant heat generation has beenconsidered for the tumor and finite element analysis has been conducted by theABAQUS software linked with a PYTHON script on both tumor models to studytemperature variations on the top tissue surface. This temperature distributionhas been characterized by 10 parameters. In each scenario, 98 sets of theseparameters has been used as inputs of a radial basis function neural network(RBFNN) and number of sides or wings has been selected to be the output. TheRBFNN has been trained to identify malignancy of tumor based on its morphology.According to the RBFNN results, the proposed method has been capable ofdifferentiating between benign and malignant tumors and estimating the degreeof malignancy with high accuracy

Thermal Analysis of Malignant Brain Tumors by Employing a Morphological Differentiation-Based Method in Conjunction with Artificial Neural Network