Title: Inertia weight updated mayfly optimisation algorithm-based thermal breast cancer image segmentation

Authors: I. Jayagayathri; C. Mythili

Addresses: Department of ECE, University College of Engineering, Nagercoil, India ' Department of EEE, University College of Engineering, Nagercoil, India

Abstract: Infrared thermography has been seen as an effective screening modality for detecting breast cancer as it identifies even very small tumours and hence leads to early diagnosis. It is non-invasive, safe, and painless that brings valuable information about the location of malignant substances. The application of deep learning algorithms in thermography is found to increase the accuracy of detection. This paper proposes an automated and optimised convolution neural network-based system for tumour segmentation of the breast thermogram images. Moreover, it is optimised with a recent bio-inspired optimisation called the mayfly algorithm. The optimisation techniques sometimes trap local optima, attain premature convergence, and cannot trade-off between the exploration and exploitation phase. In the proposed work, these problems can be overcome through an inertia weight updation policy. The effectiveness of the suggested approach has experimented on publicly accessible thermal image breast cancer datasets. The results proved that this technique can be helpful for physicians to make better decisions on breast cancer diagnosis.

Keywords: breast cancer; computer-aided diagnosis; infrared thermography; deep learning; convolutional neural network; CNN; mayfly algorithm; segmentation.

DOI: 10.1504/IJBIC.2023.135469

International Journal of Bio-Inspired Computation, 2023 Vol.22 No.3, pp.139 - 151

Received: 09 May 2022
Accepted: 02 Aug 2023
Published online: 14 Dec 2023 *

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