Extreme learning machine for cancer classification in Mammograms based on Fractal and GLCM Features - D, Saraswathi.; E, Srinivasan.; P, Ranjitha. (IJRDO -JOURNAL OF HEALTH SCIENCES AND NURSING, 2017)
Breast cancer is becoming a leading cause of death among women in the whole world;meanwhile, it is confirmed that the early detection and accurate diagnosis of this disease can ensure along survival of the patients. In this work, classification of Malignant, Benign and normal Images ofMammogram is performed. Extreme Learning Machine is developed for single hidden layer feedforwardneural network and connection between the input layer and hidden neurons are randomly assigned andremain unchanged during the learning period. In this work, noises are removed by using Curvelettransform and Region of Interest (ROI) mammogram images using manual segmentation. By usingFractal and GLCM features are extracted from the Region of Interest (ROI) and the classification isperformed by using Extreme Learning Machine (ELM) Algorithm. ELM is used to classifies the inputimages into normal and abnormal. A set of images (150) from Mammographic Image Analysis Society(MIAS) database is used for evaluating the system. The performance is analyzed using classificationaccuracy rate and reducing the false positive rate. The experimental results are compared with wavelettransform and Support Vector Machine Method. The result shows that Curvelet Transform withExtreme Machine Learning (ELM) has high classification accuracy rates of 98.3% than the existingmethod for diagnosing the breast cancer. This system will help the radiologist to diagnose the breastcancer in an efficient way.