Title: Breast cancer detection and classification using optimisation enabled deep learning model

Authors: Dhanya Mathew; K.S. Vijula Grace; M. Mary Synthuja Jain Preetha

Addresses: Department of Electronics and Communication, Noorul Islam Centre for Higher Education, Kumaracoil, 629180, India ' Department of Electronics and Communication, Noorul Islam Centre for Higher Education, Kumaracoil, 629180, India ' Department of Electronics and Communication, Noorul Islam Centre for Higher Education, Kumaracoil, 629180, India

Abstract: This work aspires to discover a scheme for breast cancer disease detection and presents the integration of deep learning methods to minimise breast cancer risk in women's lives. The initial phase involves pre-processing the images through contrast enhancement and resizing. Subsequently, the pre-processed outputs undergo data augmentation, which has become a significant focus in recent deep learning domain. This step is essential for enhancing the diversity of the original dataset. Subsequently, for segmenting cancer region in the augmented images, the multi-input shape convolution neural network (MIScnn) scheme was employed. Finally, the breast cancer detection process is performed using the hybrid LeNet-ZFNet, which is the combination of LeNet and ZFNet scheme, to classify as either normal or malignant. Finally, experimental analysis revealed that proposed approach achieved maximal accuracy of 95.8%, sensitivity of 97.3%, and specificity of 93.3%.

Keywords: breast cancer; detection; deep learning; classification; MIScnn.

DOI: 10.1504/IJBRA.2026.152611

International Journal of Bioinformatics Research and Applications, 2026 Vol.22 No.2, pp.107 - 125

Received: 11 May 2024
Accepted: 23 Sep 2024
Published online: 31 Mar 2026 *

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