TRI-BCC: Tri-Level Breast Cancer Classification via Transfer Learning Networks with Histopathological Images

Class type	Subtype	Original count	Augmented count	Total count
Benign	AS	444	400	844
Benign	FA	1442	0	1442
PT	209	500	709
TA	385	300	685
	Subtotal	2480	1200	3680
Malignant	DC	3450	0	3450
Malignant	LC	626	0	626
MC	792	0	792
PC	561	0	561
	Subtotal	5429	0	5429
Total		7909	0	9109

Flowchart of the proposed GWO algorithm.

Experimental results of the proposed TRI-BCC model for BC classification.

Table 4.

Efficiency analysis of the proposed TRI-BCC model for Level-I.

Classes	ACC	SPE	SEN	PRE	F1S
Benign	99.25	99.01	98.73	98.45	98.18
Malignant	98.84	98.06	99.01	99.27	99.49
Average	99.04	98.95	98.69	99.49	98.47

Level-II analysis of the proposed TRI-BCC model for (a) Benign classes and (b) Malignant classes.

Level-III analysis of the proposed TRI-BCC model for Malignant class stages.

Training and testing curve of the proposed TRI-BCC model (a) Accuracy curve; (b) Loss curve.

Table 5.

Comparative analysis of optimization algorithms based on DS and JS.

Metrics	FFO [24]	AO [25]	BESO [26]	GWO (ours)
Dice score	0.82	0.85	0.87	0.91
Jaccard score	0.76	0.79	0.81	0.87

Table 6.

Comparative evaluation of different ML classification models.

Methods	ACC	SPE	SEN	PRE	F1S
NB	87.9	86.8	88.4	86.3	87.2
DT	94.7	94.0	95.5	93.1	94.2
RF	96.3	95.9	96.8	95.4	96.1
KNN	89.5	89.0	90.2	88.7	89.1
RDT	99.04	98.9	98.6	99.4	98.4

Visual comparison of different optimization algorithms for segmentation.

Table 7.

Accuracy comparison: proposed model vs existing models.

Authors	Methods	Accuracy
Rahman et al.	U-Net + YOLO	93.00 %
Abunasser et al.	Fine-tuned networks	98.28 %
Singh et al.	Hybrid deep neural network	96.42 %
Hirra et al.	Pa-DBN-BC	86.00 %
Proposed model	TRI-BCC model	99.06 %

References

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Articles in this issue

DOI: https://doi.org/10.2478/msr-2025-0036 | Journal eISSN: 1335-8871

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Language: English

Page range: 327 - 337

Submitted on: Dec 11, 2024

Accepted on: Sep 11, 2025

Published on: Nov 13, 2025

Published by: Slovak Academy of Sciences, Institute of Measurement Science

In partnership with: Paradigm Publishing Services

Publication frequency: Volume open

Keywords:

breast cancer,

transfer learning,

histopathological images,

tri-level classification,

Golden Whale Optimization algorithm

Related subjects:

Engineering,

Electrical engineering,

Control engineering, metrology and testing

© 2025 Sridevi Rajalingam, Kavitha Maruthai, published by Slovak Academy of Sciences, Institute of Measurement Science
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Volume 25 (2025): Issue 6 (December 2025)

TRI-BCC: Tri-Level Breast Cancer Classification via Transfer Learning Networks with Histopathological Images

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