Mammogram mass segmentation and detection using Legendre neural network-based optimal threshold

Breast cancer is a leading cause of mortality affecting women across the world. Early detection and diagnosis can decrease the mortality rate due to this cancer. Machine learning-based models are gaining popularity for biomedical applications due to the ability of nonlinear mapping between input and output patterns using supervised training phase. The research work in the paper is focused on the optimal adaptive threshold for mammogram mass segmentation, and detection in order to assist radiologist in accurate diagnosis Legendre neural network with single layer is used to develop the model, and the training is performed through Block Based Normalized Sign-Sign Least Mean Square (BBNSSLMS) algorithm. Legendre neural network expands the input vector using standard Legendre polynomial, and the recursive update principle is followed for the weight vector in higher dimension. The optimal threshold is indirectly used for proper segmentation of mammogram mass. The proposed segmentation method involves training phase with 30 images and testing phase by 151 images obtained from standard Mammogram Image Analysis Society (MIAS) database. The proposed model achieved a sensitivity of 95% and accuracy of 96% with false positives per image calculated as 1.19.

Automated summary

The research focuses on developing a breast mammogram mass segmentation model using Legendre neural network trained with the BBNSSLMS algorithm. The proposed model achieved a sensitivity of 95% and accuracy of 96% through training with 30 images and testing with 151 images from the MIAS database.