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Cancer Disease Prediction using Mammogram Images using Intelligence Technology


Affiliations
1 Department of Computer and Information Engineering, University of Mosul, Iraq
2 Department of Computer and Information Engineering, University of Mosul, Iraq
     

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Breast cancer is a malignant (cancer) tumor starting with breast cells. A screening mammogram aims to find breast cancer when it’s too small to be felt by a woman or her doctor. Cost effectiveness is one of the major requirements for a mass screening program to be successful. The ultimate diagnosis of all types of breast disease depends on a biopsy. In most cases the decision for a biopsy is based on mammography findings. Biopsy results indicate that 65-90% of suspected cancer detected by mammography turned out to be benign. Hence, it would be valuable to expand a computer aided technique for mass classification based on extracted features from the Region Of Interests (ROI) in mammograms. This can reduce the number of unnecessary biopsies in patients with benign disease and thus avoid patients physical and mental suffering, with an added bonus of reducing healthcare costs. The study reveals that proposed work is better than the traditional models and discussed with limitations.


Keywords

Cancer Classification, Artificaila Intelligence, Machine Vision, Mammogram Images.
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  • Cancer Disease Prediction using Mammogram Images using Intelligence Technology

Abstract Views: 216  |  PDF Views: 1

Authors

Theingi Zin
Department of Computer and Information Engineering, University of Mosul, Iraq
U Soe Nay Lin Aung
Department of Computer and Information Engineering, University of Mosul, Iraq

Abstract


Breast cancer is a malignant (cancer) tumor starting with breast cells. A screening mammogram aims to find breast cancer when it’s too small to be felt by a woman or her doctor. Cost effectiveness is one of the major requirements for a mass screening program to be successful. The ultimate diagnosis of all types of breast disease depends on a biopsy. In most cases the decision for a biopsy is based on mammography findings. Biopsy results indicate that 65-90% of suspected cancer detected by mammography turned out to be benign. Hence, it would be valuable to expand a computer aided technique for mass classification based on extracted features from the Region Of Interests (ROI) in mammograms. This can reduce the number of unnecessary biopsies in patients with benign disease and thus avoid patients physical and mental suffering, with an added bonus of reducing healthcare costs. The study reveals that proposed work is better than the traditional models and discussed with limitations.


Keywords


Cancer Classification, Artificaila Intelligence, Machine Vision, Mammogram Images.