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

Currently, the death rate of cancer diseases like brain cancer, lung cancer, skin cancer and breast cancer is increasing. To analyze the internal functionality of tumors, there are many modalities like Computer Tomography, Magnetic Resonance Imaging, Ultra Sonic, Mammography and Poisson Emission Tomography. They help radiologists ensure the presence of cancer and reduce the severity by early diagnosis. Our proposed approach extracts the tumor region of multimodal images of breast and brain using Contrast Limited Adaptive Histogram Equalization with Cuckoo Search Optimization. The extracted features are fed to K nearest neighbor classifier to codify the tumor as benign or malignant. Our approach produces 98.4% and 97.6% accuracy for segmentation and classification, respectively. The computational time for segmentation is also comparable with the existing approaches like SOMFCM and PSOFCM and classification with Form feed neural network and SVM. We utilize website like BRATS, Harvard brain dataset and RIDER for validation.

Segmenting and Classifying MRI Multimodal Images Using Cuckoo Search Optimization and KNN Classifier