Breast Cancer Detection Mammogram Imagesusing Convolution Neural Network

Abstract

One in eight women globally develop breast cancer. By identifying the cancer of the breast tissue cells, it is diagnosed.Utilizing various algorithms and methodologies, modern medical image processing systems examine histopathology images that have been recorded by a microscope.Medical imaging and pathology tools are being processed using machine learning techniques.Computer-aided methods are used to achieve better outcomes than manual pathological detection systems since manually identifying a cancer cell is a laborious operation and entails human mistake. Transfer learning and fine-tuning can also be used to get the most out of a CNN that has already been trained. The first is to develop simple models or adapt existing ones to reduce the time investment and the number of training instances.In deep learning, this is typically accomplished by first extracting features with the assistance of a convolutional neural network (CNN), and then categorizing data with the assistance of a fully connected network. The field of medical imaging makes extensive use of the technique of deep learning because it does not necessitate prior knowledge in a field that is related to it. Within the scope of this investigation, we trained a convolutional neural network to generate forecasts that had an accuracy of up to 88.86%.