Improved noise reduction, Segmentation and classification of cancer masses by quantum Inverse MFT, social spider and ELM

medical intelligence detection systems have been changed and also faced with some challenges. Breast cancer diagnosis and classification is one of these medical intelligence system. Early detection can increases the options for curative treatment. There are a variety of screening techniques available to detect breast cancer such as mammography, magnetic resonance imaging and ultrasound. These images have been presented for a variety of applications and different processing techniques to date based on the type of cancer diagnosis. This research used MIAS mammography image dataset and try to diagnose and classify benign, malignant and suspicious masses based on image processing and machine learning techniques. So, a new developed approach proposed which at first, apply pre-processing for noise reduction and image enhancement based on Quantum Inverse MFT, and then image segmentation with Social Spider Algorithm based on two features such as brightness and edges apply. Then two main parts of diagnosis and classification apply which based on ELM and MPM-ELM. Obtained results presented that proposed approach have better performance in comparison to others based on some evaluation criteria such as accuracy, sensitivity, specificity and also ROC and AUC