TY - JOUR
KW - Feature Selection
KW - Mammogram
KW - Breast Cancer
KW - Computer Aided Detection
KW - Immune Recognition
KW - Fuzzy Segmentation
AU - Hamdadou Djamila
AU - Leila Belkhodja
AB - Computer Aided Detection (CAD) systems are very important tools which help radiologists as a second reader in detecting early breast cancer in an efficient way, specially on screening mammograms. One of the challenging problems is the detection of masses, which are powerful signs of cancer, because of their poor apperance on mammograms. This paper investigates an automatic CAD for detection of breast masses in screening mammograms based on fuzzy segmentation and a bio-inspired method for pattern recognition: Artificial Immune Recognition System. The proposed approach is applied to real clinical images from the full field digital mammographic database: Inbreast. In order to validate our proposition, we propose the Receiver Operating Characteristic Curve as an analyzer of our IMCAD classifier system, which achieves a good area under curve, with a sensitivity of 100% and a specificity of 95%. The recognition system based on artificial immunity has shown its efficiency on recognizing masses from a very restricted set of training regions.
IS - Regular Issue
M1 - 5
N2 - Computer Aided Detection (CAD) systems are very important tools which help radiologists as a second reader in detecting early breast cancer in an efficient way, specially on screening mammograms. One of the challenging problems is the detection of masses, which are powerful signs of cancer, because of their poor apperance on mammograms. This paper investigates an automatic CAD for detection of breast masses in screening mammograms based on fuzzy segmentation and a bio-inspired method for pattern recognition: Artificial Immune Recognition System. The proposed approach is applied to real clinical images from the full field digital mammographic database: Inbreast. In order to validate our proposition, we propose the Receiver Operating Characteristic Curve as an analyzer of our IMCAD classifier system, which achieves a good area under curve, with a sensitivity of 100% and a specificity of 95%. The recognition system based on artificial immunity has shown its efficiency on recognizing masses from a very restricted set of training regions.
PY - 2019
SP - 97
EP - 108
T2 - International Journal of Interactive Multimedia and Artificial Intelligence
TI - IMCAD: Computer Aided System for Breast Masses Detection based on Immune Recognition
UR - https://www.ijimai.org/journal/sites/default/files/files/2018/12/ijimai_5_5_12_pdf_14881.pdf
VL - 5
SN - 1989-1660
ER -