A Novel Fusion-Based Feature Extraction with an Enhanced Segmentation Method to Improve Breast Cancer Analysis Using Thermogram Imagery
Received: 2 August 2025 | Revised: 20 August 2025, 9 September 2025, and 13 September 2025 | Accepted: 15 September 2025 | Online: 8 December 2025
Corresponding author: Preethi Veerlapalli
Abstract
Breast Cancer (BC) is a common illness that has received considerable attention in the last few years. Early recognition of this disease is crucial to improving the chances of survival. Thermography is an effective screening tool that can assist in detecting BC by identifying body parts with abnormal temperature variations. For effective BC detection using thermography, it is crucial to identify the Region Of Interest (ROI) in the thermograms before diagnosis. Deep Learning (DL) models currently enable the development of systems for detecting abnormalities from thermogram images. In this study, a Leveraging Antlion Optimization and Fusion Feature for an Enhanced Segmentation Method to Improve Breast Cancer Analysis (LALOFF-SMIBCA) is proposed. This study aims to develop an automatic segmentation model for BC detection using thermogram images, thereby enhancing diagnostic accuracy and efficiency. Initially, the image pre-processing utilizes the Adaptive Bilateral Filtering (ABF) model to remove unwanted noise and enhance image quality. Furthermore, the improved DeepLabv3+ model is employed for segmentation. Moreover, a fusion of the ResNet101, ContextNet, and MobileNetV2 models is implemented for feature extraction. Additionally, a Variational Autoencoder (VAE) is utilized for BC classification. Finally, the parameter tuning process is performed using the utilization optimization method. Experiments with the LALOFF-SMIBCA approach are conducted on the BC diagnosis dataset. The comparison analysis of the LALOFF-SMIBCA approach demonstrated a superior accuracy of 98.54% compared to existing models.
Keywords:
breast cancer analysis, segmentation method, antlion optimisation, thermogram imagery, fusion feature extractionDownloads
References
D. Sánchez-Ruiz, I. Olmos-Pineda, and J. A. Olvera-López, "Automatic region of interest segmentation for breast thermogram image classification," Pattern Recognition Letters, vol. 135, pp. 72–81, July 2020. DOI: https://doi.org/10.1016/j.patrec.2020.03.025
N. Venkatachalam, L. Shanmugam, G. C. Heltin, G. Govindarajan, and P. Sasipriya, "Enhanced Segmentation of Inflamed ROI to Improve the Accuracy of Identifying Benign and Malignant Cases in Breast Thermogram," Journal of Oncology, vol. 2021, no. 1, 2021, Art. no. 5566853. DOI: https://doi.org/10.1155/2021/5566853
O. I. Ramadan et al., "Enhancing Breast Cancer Classification based on BPSO Feature Selection and Machine Learning Techniques," Engineering, Technology & Applied Science Research, vol. 15, no. 3, pp. 23907–23916, June 2025. DOI: https://doi.org/10.48084/etasr.10900
A. S. Hakim and R. N. Awale, "Extraction of hottest blood vessels from breast thermograms using state-of-the-art image segmentation methods," Quantitative InfraRed Thermography Journal, vol. 19, no. 5, pp. 347–365, Oct. 2022. DOI: https://doi.org/10.1080/17686733.2021.1974209
T. Trongtirakul, S. Agaian, A. Oulefki, T. Trongtirakul, S. Agaian, and A. Oulefki, "Automated tumor segmentation in thermographic breast images," Mathematical Biosciences and Engineering, vol. 20, no. 9, pp. 16786–16806, 2023. DOI: https://doi.org/10.3934/mbe.2023748
K. F. F. C. da Queiroz, J. R. A. de Queiroz Júnior, H. Dourado, and R. de C. F. de Lima, "Automatic segmentation of region of interest for breast thermographic image classification," Research on Biomedical Engineering, vol. 39, no. 1, pp. 199–208, Mar. 2023. DOI: https://doi.org/10.1007/s42600-023-00265-z
S. M. Shaaban, M. Nawaz, Y. Said, and M. Barr, "An Efficient Breast Cancer Segmentation System based on Deep Learning Techniques," Engineering, Technology & Applied Science Research, vol. 13, no. 6, pp. 12415–12422, Dec. 2023. DOI: https://doi.org/10.48084/etasr.6518
D. Tsietso, A. Yahya, and R. Samikannu, "A Review on Thermal Imaging-Based Breast Cancer Detection Using Deep Learning," Mobile Information Systems, vol. 2022, no. 1, 2022, Art. no. 8952849. DOI: https://doi.org/10.1155/2022/8952849
T.-N. Nguyen, T.-T. Nguyen, T.-H. Nguyen, and B.-V. Ngo, "A Robust Approach for Breast Cancer Classification from DICOM Images," Engineering, Technology & Applied Science Research, vol. 15, no. 3, pp. 23499–23505, June 2025. DOI: https://doi.org/10.48084/etasr.10931
S. Kaur, J. D. Kumar, G. Chaudhary, and M. Khari, "Breast Cancer Detection Using Deep Learning and Feature Decision Level Fusion," Fusion: Practice and Applications, no. Issue 1, pp. 50–59, Jan. 2022. DOI: https://doi.org/10.54216/FPA.080105
M. Sabry et al., "Enhancing Breast Cancer Diagnosis With Multi-Resolution Vision Transformers and Robust Decision-Making," IEEE Access, vol. 13, pp. 89704–89722, 2025. DOI: https://doi.org/10.1109/ACCESS.2025.3570840
T. Andreadis, K. Chouchos, N. Courcoutsakis, I. Seimenis, and D. Koulouriotis, "Development of an Automated CAD System for Lesion Detection in DCE-MRI," Journal of Imaging Informatics in Medicine, Feb. 2025. DOI: https://doi.org/10.1007/s10278-025-01445-2
H. Mahichi, V. Ghods, M. K. Sohrabi, and A. Sabbaghi, "BreastCNet: Breast Cancer Detection, Classification, and Localization Convolutional Neural Network With Advanced Optimization Techniques," IEEE Access, vol. 13, pp. 87386–87400, 2025. DOI: https://doi.org/10.1109/ACCESS.2025.3570364
S. S. Chowa, S. Azam, S. Montaha, M. R. I. Bhuiyan, and M. Jonkman, "Improving the Automated Diagnosis of Breast Cancer with Mesh Reconstruction of Ultrasound Images Incorporating 3D Mesh Features and a Graph Attention Network," Journal of Imaging Informatics in Medicine, vol. 37, no. 3, pp. 1067–1085, June 2024. DOI: https://doi.org/10.1007/s10278-024-00983-5
A. Modi et al., "Multi-Stain Multi-Level Convolutional Network for Multi-Tissue Breast Cancer Image Segmentation." arXiv, June 09, 2024.
M. A. Ortega-Ruíz, C. Karabağ, E. Roman-Rangel, and C. C. Reyes-Aldasoro, "DRD-UNet, a UNet-Like Architecture for Multi-Class Breast Cancer Semantic Segmentation," IEEE Access, vol. 12, pp. 40412–40424, 2024. DOI: https://doi.org/10.1109/ACCESS.2024.3377428
M. S. Hossain et al., "Region of interest (ROI) selection using vision transformer for automatic analysis using whole slide images," Scientific Reports, vol. 13, no. 1, July 2023, Art. no. 11314. DOI: https://doi.org/10.1038/s41598-023-38109-6
M. Ahmadi et al., "Comparative Analysis of Segment Anything Model and U-Net for Breast Tumor Detection in Ultrasound and Mammography Images." arXiv, Feb. 13, 2024. DOI: https://doi.org/10.1111/coin.70145
V. Dubey, D. S. Rathore, and D. R. Gedam, "A Novel Fuzzy Fusion-Based Segmentation with Enhanced Feature Selection for Improved Breast Cancer Detection," Cuestiones de Fisioterapia, vol. 54, no. 2, pp. 513–532, Jan. 2025.
T. Varshney, K. Verma, A. Kaur, and S. K. Puri, "Hybrid and optimized feature fusion for enhanced breast cancer classification," Network Modeling Analysis in Health Informatics and Bioinformatics, vol. 14, no. 1, Aug. 2025, Art. no. 81. DOI: https://doi.org/10.1007/s13721-025-00573-7
A. V. Ikechukwu, S. Bhimshetty, D. R, and M. V. Mala, "Advances in Thermal Imaging: A Convolutional Neural Network Approach for Improved Breast Cancer Diagnosis," in 2024 International Conference on Distributed Computing and Optimization Techniques (ICDCOT), Mar. 2024, pp. 1–7. DOI: https://doi.org/10.1109/ICDCOT61034.2024.10515323
A. Y. A. Bani Ahmad, J. A. Alzubi, M. Vasanthan, S. B. Kondaveeti, J. Shreyas, and T. P. Priyanka, "Efficient hybrid heuristic adopted deep learning framework for diagnosing breast cancer using thermography images," Scientific Reports, vol. 15, no. 1, Apr. 2025, Art. no. 13605. DOI: https://doi.org/10.1038/s41598-025-96827-5
S. Abdel-Khalek, "Leveraging Quantum Neural Networks with Deep Learning Based Edge Detection Model for Breast Cancer Screening using Digital Mammograms," Journal of Intelligent Systems and Internet of Things, no. Issue 2, pp. 68–81, Jan. 2025. DOI: https://doi.org/10.54216/JISIoT.160206
Y. Zhong et al., "Deep Learning-Based Layout Analysis Method for Complex Layout Image Elements," Applied Sciences, vol. 15, no. 14, Jan. 2025, Art. no. 7797. DOI: https://doi.org/10.3390/app15147797
T. Ye, M. He, Y. Wang, J. Wang, L. Zhu, and J. Zhang, "An Optimized Intelligent Segmentation Algorithm for Concrete Cracks Based on Transformer," Electronics, vol. 14, no. 9, Jan. 2025, Art. no. 1720. DOI: https://doi.org/10.3390/electronics14091720
M. F. Ghobrial, A. M. Gody, and S. T. Muhammad, "Comparative Study on End-to-End Speech Recognition Using Pre-trained Models," Fayoum University Journal of Engineering, vol. 8, no. 1, pp. 131–142, Jan. 2025. DOI: https://doi.org/10.21608/fuje.2024.312102.1089
S. Yang et al., "Optimizing foreign fiber segmentation performance with DeepLab V3+ and GAN in industrial IoE environments," Digital Communications and Networks, Mar. 2025. DOI: https://doi.org/10.1016/j.dcan.2025.03.005
J. Zhao, Y. Song, and E. Cohen, "Variational Prefix Tuning for diverse and accurate code summarization using pre-trained language models," Journal of Systems and Software, vol. 229, Nov. 2025, Art. no. 112493. DOI: https://doi.org/10.1016/j.jss.2025.112493
A. Abdelaziz, A. N. Mahmoud, V. Santos, and M. M. Freire, "Integrating temporal convolutional networks with metaheuristic optimization for accurate software defect prediction," PLOS ONE, vol. 20, no. 5, May 2025, Art. no. e0319562. DOI: https://doi.org/10.1371/journal.pone.0319562
"Breast Cancer Diagnosis." https://www.kaggle.com/datasets/faysalmiah1721758/breast-cancer-diagnosis
Y. Mo et al., "HoVer-Trans: Anatomy-Aware HoVer-Transformer for ROI-Free Breast Cancer Diagnosis in Ultrasound Images," IEEE Transactions on Medical Imaging, vol. 42, no. 6, pp. 1696–1706, June 2023. DOI: https://doi.org/10.1109/TMI.2023.3236011
U. Naseem et al., "An Automatic Detection of Breast Cancer Diagnosis and Prognosis Based on Machine Learning Using Ensemble of Classifiers," IEEE Access, vol. 10, pp. 78242–78252, 2022. DOI: https://doi.org/10.1109/ACCESS.2022.3174599
S. Aziz, K. Munir, A. Raza, M. S. Almutairi, and S. Nawaz, "IVNet: Transfer Learning Based Diagnosis of Breast Cancer Grading Using Histopathological Images of Infected Cells," IEEE Access, vol. 11, pp. 127880–127894, 2023. DOI: https://doi.org/10.1109/ACCESS.2023.3332541
Downloads
How to Cite
License
Copyright (c) 2025 Preethi Veerlapalli, Sushama Rani Dutta
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
