A Quantum Mixed-State Self-Harnessing Physics-Guided Neural Networks with Dandelion Optimizer Model for Breast Cancer Detection Using Histopathological Image
DOI:
https://doi.org/10.63949/Keywords:
- Breast cancer,
- Histopathological images,
- Quantum Mixed-State Self-Harnessing Physics-Guided Neural Networks,
- Dandelion Optimizer,
- Trainable Self-Guided Filtering.
Abstract
Breast cancer is one of the common and life-threatening disorders in the world, as it requires proper and timely diagnosis to enhance the survival of the patient. Conventional diagnostic tools tend to be unable to evaluate high-dimensional histopathological image data, and the data is misclassified, which can lead to delayed treatment. To overcome this, the Quantum Mixed-State Self-Harnessing Physics-Guided Neural Networks with Dandelion Optimizer (QM-SS-HP-GNN-DO) is suggested to provide effective breast cancer diagnosis. To begin with, the images obtained and used in histopathology are gathered and preprocessed by Trainable Self-Guided Filtering (T-SGF) to improve the features of interest included in the BreakHis dataset. QM-SS-HP-GNN model subsequently provides classification, and Dandelion Optimizer (DO) optimizes the network weights to enhance the accuracy of the network. Experimental performance is better with an accuracy of 99.43%, precision of 99.10%, sensitivity of 99.05%, specificity of 99.20%, and F1-score of 99.08%, showing the best results compared to existing models. Overall, the suggested model offers a well-constructed, stable, and extremely precise framework of breast cancer diagnosis, and can be adapted to other medical image-based disease detection problems.
Downloads
References
[1] Miguel, J. P. M., Neves, L. A., Martins, A. S., do Nascimento, M. Z., & Tosta, T. A. A. (2023). Analysis of neural networks trained with evolutionary algorithms for the classification of breast cancer histological images. Expert Systems with Applications, 231, 120609.
[2] Al-Jabbar, M., Alshahrani, M., Senan, E. M., & Ahmed, I. A. (2023). Multi-method diagnosis of histopathological images for early detection of breast cancer based on hybrid and deep learning. Mathematics, 11(6), 1429.
[3] Karuppasamy, A., Abdesselam, A., Hedjam, R., & Al-Bahri, M. (2024). Feed-forward networks using logistic regression and support vector machine for whole-slide breast cancer histopathology image classification. Intelligence-Based Medicine, 9, 100126.
[4] Srikantamurthy, M. M., Rallabandi, V. S., Dudekula, D. B., Natarajan, S., & Park, J. (2023). Classification of benign and malignant subtypes of breast cancer histopathology imaging using hybrid CNN-LSTM based transfer learning. BMC Medical Imaging, 23(1), 19.
[5] Parshionikar, S., & Bhattacharyya, D. (2024). An enhanced multi-scale deep convolutional orchard capsule neural network for multi-modal breast cancer detection. Healthcare Analytics, 5, 100298.
[6] Ahmed, M., & Islam, M. R. (2023). A combined feature-vector-based multiple instance learning convolutional neural network in breast cancer classification from histopathological images. Biomedical Signal Processing and Control, 84, 104775.
[7] Joshi, S. A., Bongale, A. M., Olsson, P. O., Urolagin, S., Dharrao, D., & Bongale, A. (2023). Enhanced pre-trained Xception model transfer learned for breast cancer detection. Computation, 11(3), 59.
[8] Toma, T. A., Biswas, S., Miah, M. S., Alibakhshikenari, M., Virdee, B. S., Fernando, S., Rahman, M. H., Ali, S. M., Arpanaei, F., Hossain, M. A., & Rahman, M. M. (2023). Breast cancer detection based on simplified deep learning technique with histopathological images using BreaKHis database. Radio Science, 58(11), 1–18.
[9] Kausar, T., Lu, Y., & Kausar, A. (2023). Breast cancer diagnosis using lightweight deep convolution neural network model. IEEE Access, 11, 124869–124886.
[10] Chikkala, R. B., Anuradha, C., Murty, P. S. C., Rajeswari, S., Rajeswaran, N., Murugappan, M., & Chowdhury, M. E. (2025). Enhancing breast cancer diagnosis with bidirectional recurrent neural networks: A novel approach for histopathological image multi-classification. IEEE Access.
[11] Maleki, A., Raahemi, M., & Nasiri, H. (2023). Breast cancer diagnosis from histopathology images using deep neural network and XGBoost. Biomedical Signal Processing and Control, 86, 105152.
[12] Karacan, L. (2023). Trainable self-guided filter for multi-focus image fusion. IEEE Access, 11, 139466–139477.
[13] Chen, F., Zhao, Q., Feng, L., Chen, C., Lin, Y., & Lin, J. (2025). Quantum mixed-state self-attention network. Neural Networks, 185, 107123.
[14] Hettige, K. H., Ji, J., Xiang, S., Long, C., Cong, G., & Wang, J. (2024). Airphynet: Harnessing physics-guided neural networks for air quality prediction. arXiv preprint, arXiv:2402.03784.
[15] Zhao, S., Zhang, T., Ma, S., & Chen, M. (2022). Dandelion optimizer: A nature-inspired metaheuristic algorithm for engineering applications. Engineering Applications of Artificial Intelligence, 114, 105075.
