Open AccessCOMPUTER-AIDED MODEL FOR BREAST CANCER DETECTION IN MAMMOGRAMS
Author(s) -
S. Nithya,
Meenakshi Sundaram Muthuraman
Publication year - 2016
Publication title -
international journal of pharmacy and pharmaceutical sciences/international journal of pharmacy and pharmaceutical sciences
Language(s) - English
Resource type - Journals
eISSN - 2656-0097
pISSN - 0975-1491
DOI - 10.22159/ijpps.2016v8s2.15216
Subject(s) - artificial intelligence , segmentation , pattern recognition (psychology) , mammography , region of interest , classifier (uml) , computer science , breast cancer , computer aided diagnosis , feature extraction , feature selection , medicine , cancer
The objective of this research was to introduce a new system for automated detection of breast masses in mammography images. The system will be able to discriminate if the image has a mass or not, as well as benign and malignant masses. The new automated ROI segmentation model, where a profiling model integrated with a new iterative growing region scheme has been proposed. The ROI region segmentation is integrated with both statistical and texture feature extraction and selection to discriminate suspected regions effectively. A classifier model is designed using linear fisher classifier for suspected region identification. To check the system’s performance, a large mammogram database has been used for experimental analysis. Sensitivity, specificity, and accuracy have been used as performance measures. In this study, the methods yielded an accuracy of 93% for normal/abnormal classification and a 79% accuracy for bening/malignant classification. The proposed model had an improvement of 8% for normal/abnormal classification, and a 7% improvement for benign/malignant classification over Naga et al. , 2001. Moreover, the model improved 8% for normal/abnormal classification over Subashimi et al. , 2015. The early diagnosis of this disease has a major role in its treatment. Thus the use of computer systems as a detection tool could be viewed as essential to helping with this disease.