Title: LARSE: level-based associated residual network with squeeze-and-excitation for breast cancer detection and classification

Authors: P.S. Anu Rakhi; R.S. Rajesh

Addresses: Department of Computer Science and Engineering, Manonmaniam Sundaranar University, Tirunelveli, Tamil Nadu, India ' Department of Computer Science and Engineering, Manonmaniam Sundaranar University, Tirunelveli, Tamil Nadu, India

Abstract: Breast cancer is considered as a serious disease causing a high mortality rate amongst women. In recent years, computer aided diagnosis (CAD) techniques have the radiologists to make proper decisions on mammograms more accurately. The existing CAD method may not contribute significant results for the early identification of breast mass especially at stages 1 and 2. This work introduces a level-based associated residual network with squeeze-and-excitation (LARSE) block for breast cancer classification. Initially, the input image undergoes pre-processing using the contrast limited adaptive histogram equalisation (CLAHE) model. Then, the feature extraction process is done by utilising a dual ResNet-based feature extraction model, LARSE. The LARSE model is used for multilevel breast cancer classification based on BI.RADS categories, tested on CBIS.DDSM and INbreast mammogram datasets. The LARSE model achieved an accuracy of 96.9% (±0.5%), sensitivity of 98% (±0.4%), specificity of 94% (±0.6%), and F1.score of 96% (±0.3%).

Keywords: mammography; computer aided design; CAD; breast cancer; classification; deep learning.

DOI: 10.1504/IJBRA.2025.148125

International Journal of Bioinformatics Research and Applications, 2025 Vol.21 No.4, pp.415 - 435

Received: 18 Apr 2024
Accepted: 01 Jul 2024
Published online: 26 Aug 2025 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article