Using Deep Learning Boosting with Survival Analysis for Breast Cancer Diagnosis

Mahdi S. Raza; Abbas M. Ali; Chiman Haydar Salh

doi:10.21271/ZJPAS.37.5.8

Authors

Mahdi S. Raza Department of Software Engineering and Informatics, College of Engineering, Salahaddin University-Erbil, Erbil 44001, Iraq
Abbas M. Ali Department of Software Engineering and Informatics, College of Engineering, Salahaddin University-Erbil, Erbil 44001, Iraq
Chiman Haydar Salh Department of Information System Engineering, Erbil Technical Engineering College, Erbil Polytechnic University, Erbil 44001, Iraq

DOI:

https://doi.org/10.21271/ZJPAS.37.5.8

Keywords:

Survival analysis, Cox. Regression model, Detectron2, Mask R-CNN, MRI, Breast Cancer, Tumor size, and Boosting Mask RCNN.

Abstract

Around the world, women are diagnosed with breast cancer, which is a deadly disease. Early detection is very important in helping improve the survival rate and advance the excellence of care for patients. With the help of deep learning models, medical image analysis can be performed with high accuracy and can even be used to automatically identify breast cancer. Several research has been undertaken on the application of survival analysis and deep learning models for the detection of breast cancer. This work introduces a methodology that can effectively and precisely diagnose breast cancer with reliability. This approach utilizes survival analysis and Deep learning, enhanced with statistical techniques such as the Cox regression model and Kaplan Meier Method. Additionally, a dataset of breast cancer magnetic resonance imaging (MRI) has been created and refined. The study findings demonstrated that the utilized model for the survival analysis of breast cancer exhibited excellent performance, achieving a remarkable accuracy rate of 98%. The improved Mask R-CNN with the scanner known as enabled the comparison of tumor size and breast size.

References

Ackermann, M., Iren, D., Wesselmecking, S., Shetty, D. & Krupp, U. 2022. Automated segmentation of martensite-austenite islands in bainitic steel. Materials Characterization, 191, 112091.

Alikhassi, A., Gourabi, H. E. & Baikpour, M. 2018. Comparison of inter-and intra-observer variability of breast density assessments using the fourth and fifth editions of Breast Imaging Reporting and Data System. European journal of radiology open, 5, 67-72.

Altini, N., Brunetti, A., Puro, E., Taccogna, M. G., Saponaro, C., Zito, F. A., De Summa, S. & Bevilacqua, V. 2022. Ndg-cam: Nuclei detection in histopathology images with semantic segmentation networks and grad-cam. Bioengineering, 9, 475.

Avcı, H. & Karakaya, J. 2023. A novel medical image enhancement algorithm for breast cancer detection on mammography images using machine learning. Diagnostics, 13, 348.

Cai, H., Huang, Q., Rong, W., Song, Y., Li, J., Wang, J., Chen, J. & Li, L. 2019. Breast microcalcification diagnosis using deep convolutional neural network from digital mammograms. Computational and mathematical methods in medicine, 2019, 2717454.

Cai, L., Long, T., Dai, Y. & Huang, Y. 2020. Mask R-CNN-based detection and segmentation for pulmonary nodule 3D visualization diagnosis. Ieee Access, 8, 44400-44409.

Chen, L., Li, S., Bai, Q., Yang, J., Jiang, S. & Miao, Y. 2021. Review of image classification algorithms based on convolutional neural networks. Remote Sensing, 13, 4712.

Chiao, J.-Y., Chen, K.-Y., Liao, K. Y.-K., Hsieh, P.-H., Zhang, G. & Huang, T.-C. 2019. Detection and classification the breast tumors using mask R-CNN on sonograms. Medicine, 98, e15200.

Di Micco, R., O'connell, R., Barry, P., Roche, N., Macneill, F. & Rusby, J. 2017. Standard wide local excision or bilateral reduction mammoplasty in large-breasted women with small tumours: surgical and patient-reported outcomes. European Journal of Surgical Oncology (EJSO), 43, 636-641.

Gouveia, P. F., Oliveira, H. P., Monteiro, J. P., Teixeira, J. F., Silva, N. L., Pinto, D., Mavioso, C., Anacleto, J., Martinho, M. & Duarte, I. 2022. 3D breast volume estimation. European Surgical Research, 63, 3-8.

Ivanovska, T., Jentschke, T. G., Daboul, A., Hegenscheid, K., Völzke, H. & Wörgötter, F. 2019. A deep learning framework for efficient analysis of breast volume and fibroglandular tissue using MR data with strong artifacts. International journal of computer assisted radiology and surgery, 14, 1627-1633.

Kim, C.-M., Park, R. C. & Hong, E. J. 2020. Breast mass classification using eLFA algorithm based on CRNN deep learning model. IEEE Access, 8, 197312-197323.

Kumaraswamy, E., Kumar, S. & Sharma, M. 2023. An invasive ductal carcinomas breast cancer grade classification using an ensemble of convolutional neural networks. Diagnostics, 13, 1977.

Lin, R.-H., Kujabi, B. K., Chuang, C.-L., Lin, C.-S. & Chiu, C.-J. 2022. Application of deep learning to construct breast cancer diagnosis model. Applied Sciences, 12, 1957.

Lopez-Almazan, H., Pérez-Benito, F. J., Larroza, A., Perez-Cortes, J.-C., Pollan, M., Perez-Gomez, B., Trejo, D. S., Casals, M. & Llobet, R. 2022. A deep learning framework to classify breast density with noisy labels regularization. Computer Methods and Programs in Biomedicine, 221, 106885.

Michael, E., Ma, H., Li, H., Kulwa, F. & Li, J. 2021. Breast cancer segmentation methods: current status and future potentials. BioMed research international, 2021, 9962109.

Militello, C., Rundo, L., Dimarco, M., Orlando, A., Conti, V., Woitek, R., D’angelo, I., Bartolotta, T. V. & Russo, G. 2022. Semi-automated and interactive segmentation of contrast-enhancing masses on breast DCE-MRI using spatial fuzzy clustering. Biomedical Signal Processing and Control, 71, 103113.

Nasser, M. & Yusof, U. K. 2023. Deep learning based methods for breast cancer diagnosis: a systematic review and future direction. Diagnostics, 13, 161.

Nayak, D. R., Padhy, N., Mallick, P. K., Zymbler, M. & Kumar, S. 2022. Brain tumor classification using dense efficient-net. Axioms, 11, 34.

Qi, X., Zhang, L., Chen, Y., Pi, Y., Chen, Y., Lv, Q. & Yi, Z. 2019. Automated diagnosis of breast ultrasonography images using deep neural networks. Medical image analysis, 52, 185-198.

Raza, M. 2016. Using survival analysis to investigate breast cancer in the Kurdistan region of Iraq. City, University of London.

Raza, M. S. & Broom, M. 2023. The Use of Survival Analysis Modelling with Incomplete Data with Application to Breast Cancer. Asian Journal of Probability and Statistics, 25, 45-69.

Ren, T., Lin, S., Huang, P. & Duong, T. Q. 2022. Convolutional neural network of multiparametric MRI accurately detects axillary lymph node metastasis in breast cancer patients with pre neoadjuvant chemotherapy. Clinical breast cancer, 22, 170-177.

Saber, A., Sakr, M., Abo-Seida, O. M., Keshk, A. & Chen, H. 2021. A novel deep-learning model for automatic detection and classification of breast cancer using the transfer-learning technique. IEEe Access, 9, 71194-71209.

Salh, C. H. & Ali, A. M. 2023. Unveiling Breast Tumor Characteristics: A ResNet152V2 and Mask R-CNN Based Approach for Type and Size Recognition in Mammograms. Traitement du Signal, 40.

Surendhar, S. P. A. & Vasuki, R. 2021. Breast cancer detection using deep belief network by applying feature extraction on various classifiers. Turkish Journal of Computer and Mathematics Education, 12, 471-487.

Wang, X., Ahmad, I., Javeed, D., Zaidi, S. A., Alotaibi, F. M., Ghoneim, M. E., Daradkeh, Y. I., Asghar, J. & Eldin, E. T. 2022. Intelligent hybrid deep learning model for breast cancer detection. Electronics, 11, 2767.

Whitney, H. M., Li, H., Ji, Y., Liu, P. & Giger, M. L. 2019. Comparison of breast MRI tumor classification using human-engineered radiomics, transfer learning from deep convolutional neural networks, and fusion methods. Proceedings of the IEEE, 108, 163-177.

Yassin, N. I., Omran, S., El Houby, E. M. & Allam, H. 2018. Machine learning techniques for breast cancer computer aided diagnosis using different image modalities: A systematic review. Computer methods and programs in biomedicine, 156, 25-45.

Zhang, Y., Liu, Y.-L., Nie, K., Zhou, J., Chen, Z., Chen, J.-H., Wang, X., Kim, B., Parajuli, R. & Mehta, R. S. 2023. Deep learning-based automatic diagnosis of breast cancer on MRI using mask R-CNN for detection followed by ResNet50 for classification. Academic radiology, 30, S161-S171.

Ahmad, S. & Mouiad, A. (2021) ‘Comparative study: 2D object detection & inferencing using Detectron2’, Abstract, August, pp. 0–5.

Kumar, M., Khatri, S. & Mohammadian, M. (2023) ‘Breast cancer classification approaches-a comparative analysis’, Journal of Information Systems and Telecommunication (JIST), 41(1), pp. 2322–1437.

Pham, V., Pham, C. & Dang, T. (2020) ‘Road damage detection and classification with Detectron2 and faster R-CNN’, Proceedings of the 2020 IEEE International Conference on Big Data (Big Data 2020), pp. 5592–5601. Available at: https://doi.org/10.1109/BigData50022.2020.9378027

Salh, C.H. & Ali, A.M. (2022) ‘Comprehensive study for breast cancer using deep learning and traditional machine learning’, Zanco Journal of Pure and Applied Sciences, 34(2).

Tahir, K.F., Ali, A.M. & Hadi, G.M. (2024) ‘MASK-RCNN on the diagnoses of lung cancer in Kurdistan Region of Iraq’, Zanco Journal of Pure and Applied Sciences, 36(1).