Segmentation and classification using image processing and supervising learning framework for mitosis detection in breast cancer mammographic images

Abstract

Detection of the area withholding the mitotic cell growth is a vital marker in breast cancer detection. This paper aims to fabricate an automatic Computer-Aided Detection (CAD) [2] model that helps to locate the region of mitotic cell growth and signify the type of breast cancer found: benign or malignant. Contrary to the legacy literature [1], which uses partially supervised learning models applied to histopathology images, we devise a model which involves a fully supervised convolution model applied to mammographic images. The model trained with image datasets: benign and malignant breast cancer images. This model exploits the MIAS database and datasets collected privately from hospitals, consisting of mammographic images available as samples for breast cancer detection. Applying image segmentation techniques to the datasets, we highlight the region of interest, and thereby using classification methodologies, we separate the results as benign or malignant. The developed model equips us to yield results with an accuracy of 97.96% on the dataset.