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

Problem statement: Breast cancer is one of the most dangerous diseases that cause innumerable fatal in the female society. Early detection is the only way to reduce the mortality. Due to variety of factors sometimes manual reading of mammogram results in misdiagnosis. So that the diagnosis rate varies from 65-85%. Various computer aided detection techniques have been proposed for the past 20 years. Even then the detection rate is still not high. Approach: The proposed method consists of the following steps preprocessing, segmentation, feature extraction and classification. Noise, Artifact and pectoral region are removed in a preprocessing step. Contrast enhancement and Sobel operator with segmentation algorithm is used to segment the mass region. Feature extraction is performed on the segmented image using gray level co-occurrence matrix and local binary pattern method. Extracted features are classified using support vector machine. The performance of the proposed system is evaluated using partest method. Results: Proposed algorithm shows 98.8% sensitivity and 97.4% Specificity. Conclusion: The proposed algorithm is fully automatic and will be helpful in assisting the radiologists to detect the malignancy efficiently

A Novel Approach in Malignancy Detection of Computer Aided Diagnosis