TRANSFER LEARNING-DRIVEN DEEP NEURAL NETWORK FRAMEWORK FOR EARLY AND ACCURATE BREAST CANCER DETECTION

D.B  Shanmugam; S.  Athinarayanan; A. Josephine  Christilda; G. Manoharan

doi:10.30572/2018/KJE/170238

Authors

D.B Shanmugam Department of Data Science, SRM IST Ramapuram Campus, Chennai, India
S. Athinarayanan Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, India
A. Josephine Christilda Department of Mathematics, VelTech Multitech Dr Rangarajan Dr Sakunthala Engineering College, Avadi, Chennai, India
G. Manoharan Department of Mathematics, Jeppiaar Institute of Technology, Chennai, India

DOI:

https://doi.org/10.30572/2018/KJE/170238

Keywords:

Transfer learning, Convolutional neural network, breast cancer

Abstract

Breast cancer continues to be one of the world’s leading causes of death for women and early detection is essential to increasing survival rates and simplifying treatment. Even though they work well traditional diagnostic methods frequently require a lot of time and resources and are prone to human interpretation errors. Although training deep neural networks from scratch requires large-scale annotated datasets which are frequently unavailable in medical domains recent developments in deep learning have demonstrated great promise in medical image analysis. This work suggests a Transfer Learning-Driven Deep Neural Network Framework for early and precise breast cancer detection in order to overcome this difficulty. The framework makes use of pre-trained convolutional neural network (CNN) architectures that have been refined on publicly accessible mammography and histopathological datasets including VGG19 ResNet and DenseNet. To improve model generalization and lessen overfitting extensive preprocessing procedures like image normalization noise reduction and data augmentation are used. The suggested method outperforms traditional CNNs trained from scratch with accuracy exceeding 97% precision and recall above 96% and an F1-score of 0. 97 according to experimental evaluations on benchmark datasets like BreakHis and CBIS-DDSM. The findings demonstrate how transfer learning can enable reliable data-efficient breast cancer detection systems opening the door for clinical applications with limited resources and real-time capabilities

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References

Abbosh, Younis, et al. “KERATOCONUS DETECTION USING DEEP LEARNING”. Kufa Journal of Engineering, vol. 16, no. 2, Apr. 2025, pp. 280-94, https://doi.org/10.30572/2018/KJE/160217.

Almarri, B., Gupta, G., Kumar, R., et al. (2024). The BCPM method: Decoding breast cancer with machine learning. BMC Medical Imaging, 24, 248. https://doi.org/10.1186/s12880-024-01402-5

Atrey, K., Singh, B. K., & Bodhey, N. K. (2024). Multimodal classification of breast cancer using feature level fusion of mammogram and ultrasound images in machine learning paradigm. Multimedia Tools and Applications, 83(12), 21347–21368. https://doi.org/10.1007/s11042-023-16414-6

Ayana, G., Dese, K., & Choe, S. W. (2021). Transfer learning in breast cancer diagnoses via ultrasound imaging. Cancers, 13(4), 1–16. https://doi.org/10.3390/cancers13040738

Aymaz, S. (2024). A new framework for early diagnosis of breast cancer using mammography images. Neural Computing and Applications, 36(2), 1665–1680. https://doi.org/10.1007/s00521-023-09156-x

Bradley, A. P. (1997). The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition, 30(7), 1145–1159. https://doi.org/10.1016/S0031-3203(96)00142-2

F. A. Spanhol, L. S. Oliveira, C. Petitjean and L. Heutte, "Breast cancer histopathological image classification using Convolutional Neural Networks," 2016 International Joint Conference on Neural Networks (IJCNN), Vancouver, BC, Canada, 2016, pp. 2560-2567, doi: 10.1109/IJCNN.2016.7727519. keywords: {Neural networks},

Ghantasala, G. P., Hung, B. T., Chakrabarti, P., & Pellakuri, V. (2024). Artificial intelligence based machine learning algorithm for prediction of cancer in female anatomy. Multimedia Tools and Applications, 1–27. https://doi.org/10.1007/s11042-024-17687-3

Ghulam Murtaza, LiyanaShuib, Ainuddin Wahid Abdul Wahab, Ghulam Mujtaba, Ghulam Mujtaba, Henry Friday Nweke et al. 2020, ‘Deep learning-based breast cancer classification through medical imaging modalities: state of the art and research challenges’, Artificial Intelligence Review, vol. 53, no. 3, pp. 1655-1720

Hassan, Raghdaazad, and Ibrahim Ahmed Saleh. “PREDICTION OF SOFTWARE ANOMALIES METHODS BASED ON ENSEMBLE LEARNING METHODS”. Kufa Journal of Engineering, vol. 16, no. 3, July 2025, pp. 639-57, https://doi.org/10.30572/2018/KJE/160336.

Jakkaladiki, S. P., & Maly, F. (2023). An efficient transfer learning based cross model classification (TLBCM) technique for the prediction of breast cancer. PeerJ Computer Science, 9, e1281. https://doi.org/10.7717/peerj-cs.1281/supp-1

Jiang, Z., Yang, L., Jin, L., Yi, L., Bing, P., Zhou, J., & Yang, J. (2022). Identification of novel cuproptosis-related lncRNA signatures to predict the prognosis and immune microenvironment of breast cancer patients. Frontiers in Oncology, 12. https://doi.org/10.3389/fonc.2022.988680

Krause, J., Grabsch, H. I., Kloor, M., et al. (2021). Deep learning detects genetic alterations in cancer histology generated by adversarial networks. The Journal of Pathology, 254(1), 70–79. https://doi.org/10.1002/path.5619

Lan, J., Chen, L., Li, Z., Liu, L., Zeng, R., He, Y., & Ding, Y. (2024). Multifunctional biomimetic liposomes with improved tumor-targeting for TNBC treatment by combination of chemotherapy, antiangiogenesis and immunotherapy. Advanced Healthcare Materials, 2400046. https://doi.org/10.1002/adhm.202400046

Lei, Y., He, X., Yao, J., Wang, T., Wang, L., Li, W., Curran, W. J., Liu, T., Xu, D., & Yang, X. (2021). Breast tumor segmentation in 3D automatic breast ultrasound using Mask scoring R-CNN. Medical Physics, 48(1), 204–214. https://doi.org/10.1002/mp.14569

Leilei Zhou, Zuoheng Zhang, Yu-Chen Chen, Zhen-Yu Zhao, Xin-Dao Yin & Hong-Bing Jiang 2019, ‘A deep learning-based radiomics model for differentiating benign and malignant renal tumors’, Translational Oncology, vol. 12, no. 2, pp. 292-300

Mansour, Hassanain Shakir, et al. “A Novel Deep 2D-CNN Model for ECG-Based Arrhythmia Diagnosis With Selective Attention Mechanism and CWT Integration”. Kufa Journal of Engineering, vol. 16, no. 2, Apr. 2025, pp. 423-44, https://doi.org/10.30572/2018/KJE/160225.

Oza, P., Sharma, P., Patel, S., & Kumar, P. (2022). Deep convolutional neural networks for computer-aided breast cancer diagnostic: A survey. Neural Computing and Applications, 34(2), 1815–1836. https://doi.org/10.1007/s00521-021-06804-y

R. P and M. G, "DPKEA – A Tool for Machine Learning Applications to Improve Their Performances," 2025 7th International Conference on Intelligent Sustainable Systems (ICISS), India, 2025, pp. 812-819, doi: 10.1109/ICISS63372.2025.11076396.

Sahu, A., Das, P. K., & Meher, S. (2024). An efficient deep learning scheme to detect breast cancer using mammogram and ultrasound breast images. Biomedical Signal Processing and Control, 87, 105377. https://doi.org/10.1016/j.bspc.2023.105377

Sokolova, M., & Lapalme, G. (2009). A systematic analysis of performance measures for classification tasks. Information Processing & Management, 45(4), 427–437. https://doi.org/10.1016/j.ipm.2009.03.002

Srikantamurthy, M. M., Rallabandi, V. P. S., Dudekula, D. B., et al. (2023). Classification of benign and malignant subtypes of breast cancer histopathology imaging using hybrid CNN-LSTM based transfer learning. BMC Medical Imaging, 23, 19. https://doi.org/10.1186/s12880-023-00964-0

Woo Kyung Moon, Yan-Wei Lee, Hao-Hsiang Ke, Su Hyun Lee, Chiun-Sheng Huang & Ruey-Feng Chang 2020, ‘Computer‐aided diagnosis of breast ultrasound images using ensemble learning from convolutional neural networks’, Computer Methods and Programs in Biomedicine, vol. 190, doi: 10.1016/j.cmpb.2020.105361