Efficient breast cancer detection using novel intensity partitioning-based clustering algorithm and multi-dimensional LSTM cyclic neural network

Abstract

Recently, early detection of breast cancer is significant to reduce the mortality rate, especially in women. Hence, the study aims to classify breast cancer from digital database for screening mammography (DDSM) dataset using partition and intensity based segmentation algorithm and modified convolutional neural network-long short-term memory (CNN-LSTM) classifier. Initially, pre-processing is performed using Gaussian filtering by taking the mammogram image. Then, it is segmented using a novel intensity partitioning-based clustering algorithm (IPCA). Further, feature extraction is performed and finally, classification is implemented using a novel multi-dimensional LSTM cyclic neural network (MLSTM-CNN). The analysis is performed to evaluate the efficiency of the proposed system and the outcomes explored its efficacy in breast cancer detection.