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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Genetic Algorithm (GA) is an adopted technique inspired by the biological mechanism for practical applications at various domains of medical imaging. Classifying brain tumour tissues into heathy and non-healthy ones using features selection criteria based on genetic-based modified algorithms is highly innovative domain in medical image analysis. The classification further enhances the performance of medical experts to correctly classify the MRI scan fed to such systems that classify tumour presence and their type for fast and adequate treatment. For this purpose, numerous schemes introduced, where a binary string is considered as chromosomes. Their internal operations mimic the biological processes to generate the highly similar offspring. The fitness process assigns the value to all chromosomes to perform the selection, mutation and crossover operations. The purpose of our experimental research work is to increase the accuracy by modifying the conventional GAs to tackle randomness and to produce the highly similar offspring. This work ensures to place the most similar parents contagiously. The mutation and crossover thus ensure the highly similar offspring to their respective parents. The proposed GA ensures the placement and selection of strongest features, and high accuracy is achieved which is subjected to the evaluation of most commonly used evaluation techniques based on confusion metrics that eventually leads to the accumulation of accuracy, specificity and sensitivity respective showing optimal results as discussed in their respective section.

Modified genetic algorithm for optimal classification of abnormal MRI tissues using hybrid model with discriminative learning approach