Title: Modified VGG19 transfer learning model for breast cancer classification

Authors: Sashikanta Prusty; Srikanta Patnaik; Sujit Kumar Dash

Addresses: Department of Computer Science and Engineering, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, India ' Interscience Institute of Management and Technology (IIMT), Bhubaneswar, India ' Department of Electrical and Electronics Engineering, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, India

Abstract: Breast cancer (BC) seems to have become a sign of great concern in everyday life. There have been a lot of research and methods already designed in the last few years but continue to be prone worldwide. To address this issue, a modified version of the visual geometric group-19 (VGG19) model, namely BCNet21 has been proposed here to classify the malignant class from breast mammogram images collected from the MIAS dataset. Furthermore, the performance of our proposed BCNet21 model has been compared with the two most common predefined VGG16 and VGG19 models using the performance metrics and Cohen-Kappa test (k). The result shows that the proposed BCNet21 model outperforms with a higher accuracy of 98.96 % and a kappa score of 86%, compared to the VGG16 and VGG19 models. This concludes that the BCNet21 model is much closer to the near-perfect agreement between actual and predicted breast cancer instances.

Keywords: breast cancer; BC; deep learning; DL; transfer learning; TL; VGG19; VGG16; kappa score.

DOI: 10.1504/IJRIS.2026.150613

International Journal of Reasoning-based Intelligent Systems, 2026 Vol.18 No.1, pp.22 - 32

Received: 12 May 2023
Accepted: 04 Dec 2023
Published online: 18 Dec 2025 *

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