Breast Cancer Detection Using Neural Network Model and Transform Based Function

Abstract

The accurate detection of breast cancer saves the lives of women worldwide. Breast cancer is a worldwide leading cause of women's death in the current scenario. The automated diagnoses and image classification give the second opinion to doctor to plan treatment against breast cancer. The advancement of biomedical engineering provides the capacity for natural image classification and detection. The natural image classification and detection is part of an artificial neural network. The artificial neural network imparts various models such as supervised and unsupervised classification processes for breast cancer images. Better classification algorithms predict the better result of breast cancer cells for treatments. Transform function plays a vital role in natural image processing for recognition and decomposition of medical image data; in this paper, the study of breast cancer detection using the transform-based function with neural network models. The nature of the transform function is continuous and discrete applied to the breast cancer image's nature.  This paper details investigation of breast cancer imagery data classification with different neural network models. The classification process also estimates the limitation and divergence of vector factors to control the error rate during the process of classification. Analyzed the neural network models with MATLAB software and reputed image dataset of breast cancer.