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A novel classification scheme to decline the mortality rate among women due to breast tumor
Author(s) -
Mughal Bushra,
Sharif Muhammad,
Muhammad Nazeer,
Saba Tanzila
Publication year - 2018
Publication title -
microscopy research and technique
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.536
H-Index - 118
eISSN - 1097-0029
pISSN - 1059-910X
DOI - 10.1002/jemt.22961
Subject(s) - false positive paradox , artificial intelligence , computer science , false positives and false negatives , informatics , pattern recognition (psychology) , medicine , machine learning , data mining , electrical engineering , engineering
Early screening of skeptical masses or breast carcinomas in mammograms is supposed to decline the mortality rate among women. This amount can be decreased more on development of the computer‐aided diagnosis with reduction of false suppositions in medical informatics. Our aim is to provide a robust tumor detection system for accurate classification of breast masses using normal, abnormal, benign, or malignant classes. The breast carcinomas are classified on the basis of observed classes. This is highly dependent on feature extraction process. In propose work, a novel algorithm for classification based on the combination of top Hat transformation and gray level co‐occurrence matrix with back propagation neural network. The aim of this study is to present a robust classification model for automated diagnosis of the breast tumor with reduction of false assumptions in medical informatics. The proposed method is verified on two datasets MIAS and DDSM. It is observed that rate of false positives decreased by the proposed method to improve the performance of classification, efficiently.