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Home > Archives > Volume 20, No 8 (2022) > Article

DOI: 10.14704/nq.2022.20.8.NQ44436

Accurate Hybrid Hierarchical Model for Breast Cancer detection

Sheenum Vashist, Vikrant Sharma


The main objective of this research was to validate and develop an accurate graphical user interface based on hierarchical fuzzy breast cancer detection tool which is called as “An Accurate Hybrid Hierarchical Model for Breast cancer detection” for the diagnosis of breast cancer accurately and efficiently. Along with this it also highlights the highly affected area in each group. Two groups have been selected to diagnose the level of breast cancer which includes risk parameters and subjective parameters. The hierarchical fuzzy expert system had been developed using IF-Then rules based on the experiences of the specialists and both the groups were tested on the designed system. The third group have been developed to detect abnormalities and lumps present in Mammograms and cancerous cell images by using segmentation technique and are classified has a type of cancer by using random forest classifier. After comparing the fuzzy rules with health care experts, the hierarchical fuzzy expert system was found to be 98% accuracy, 97% sensitivity and 100% specificity respectively. After evaluating mammograms, it has been found to be 95% above accuracy and the cancerous cell image system is found to be 96% accuracy, 90% sensitivity and 89% specificity.


Breast Cancer, Mammogram, Weight function, Cancerous Cell Images, Hierarchical Fuzzy system, Image Processing.

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