Breast Cancer Classification on Mammograms Using Extreme Learning Machines
Jinn‐Yi Yeh, Yi‐Chen Hou, Yuanliang Wang · 2023
This study proposes a computer-aided diagnosis system for identifying benign and malignant breast cancer on mammograms to assist physicians. We use MIAS and DDSM databases as source image data. The process includes image preprocessing, region of interest (ROI) extraction, and tumor classification. During image pre-processing, image contrast will be enhanced, and useless color blocks such as chest muscles will be shaved off. In the ROI extraction, the suspicious tumor area is separated from the rest of the background area. The resulting image is the input data of the subsequent classification method. The classifier uses the extreme learning machine (ELM). Finally, the proposed method will be compared with artificial neural network (ANN) convolutional neural network (CNN). Experimental results show that the classification accuracy of ELM and CNN in the DDSM database is also 68%. However, The model training time of CNN is seven times that of ELM.