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

Authors: Gul Shaira Banu Jahangeer; T. Dhiliphan Rajkumar

Addresses: Department of CSE, School of Computing, Kalasalingam Academy of Research and Education, Krishnankoil, Tamilnadu, India ' Department of CSE, School of Computing, Kalasalingam Academy of Research and Education, Krishnankoil, Tamilnadu, India

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.

Keywords: intensity partitioning; segmentation; long short-term memory; LSTM; cyclic neural network; CNN; breast cancer classification; digital database for screening mammography; DDSM; intensity partitioning-based clustering algorithm; IPCA.

DOI: 10.1504/IJMEI.2023.134536

International Journal of Medical Engineering and Informatics, 2023 Vol.15 No.6, pp.549 - 563

Received: 23 May 2021
Accepted: 19 Aug 2021
Published online: 27 Oct 2023 *

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