Optimisation-driven model for breast cancer classification model using histopathological image

Sukhada Bhingarkar

International Journal of Industrial and Systems Engineering, 2025, vol. 51, issue 3, 385-412

Abstract: This paper provides a novel optimised deep model for classifying breast cancer. The simulation of IoT is the first step carried out, where the nodes collect the breast cancer histopathological image of patients. The routing is established with child circle inspired drawing optimisation (CCIDO). The fitness function is considered for choosing the best route using energy, trust distance, and delay. Then, the multi-grade breast cancer is executed at the base station. Here, a median filter is utilised for abandoning the noise. Unified extraction of features is provided for acquiring the features. The classification of breast cancer is done with the LeNet and trained using CCIDO. The assessment was performed to reveal the importance of the proposed model. The CCIDO-LeNet outperformed with the highest accuracy of 94.9%, NPV of 93.4%, PPV of 93.3%, TNR of 93.9% and TPR of 94.8%. In future, other datasets can be engaged to validate model flexibility.

Keywords: breast cancer detection; internet of things; median filter; SegNet; LeNet. (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=149969 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijisen:v:51:y:2025:i:3:p:385-412

Access Statistics for this article

More articles in International Journal of Industrial and Systems Engineering from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().