Abstract
The research has focused on breast cancer prediction using enhanced convolution neural network (CNN). The data set related to breast cancer has been considered during this research. The convolution neural network implementation has been made to predict breast cancer. CNN mechanism classifies image and breaks it down into features, reconstructed and predicted at the end. The edge-based samples have been considered to reduce the comparison time and space. This results in an increased accuracy. The introduction section introduced basic concepts of breast cancer prediction system. The existing researches in a relevant field have been represented in the second section. The motivation and challenges to research have been explained afterward. Later proposed work and results are representing the simulation of work. Simulation results have shown that edge-based image processing in the convolution neural network has reduced the time and space. The accuracy has been also increased.
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References
Jiang Y, Id LC, Zhang H, Xiao X (2019) Breast cancer histopathological image classification using convolutional neural networks with small SE-ResNet module, pp 1–21
Ragab DA, Sharkas M, Marshall S, Ren J (2019) Breast cancer detection using deep convolutional neural networks and support vector machines, pp 1–23
Dencks S, Piepenbrock M, Opacic T, Krauspe B, Stickeler E, Kiessling F (2018) Relative Blood Volume Estimation from Clinical Super-Resolution US Imaging in Breast Cancer, no. 1, pp 1–4
Lin H, Member S, Chen H, Graham S, Member S (2018) Fast ScanNet: Fast and Dense Analysis of Multi-Gigapixel Whole-Slide Images for Cancer Metastasis Detection. IEEE Trans. Med. Imaging, vol. PP, no. c, p. 1, 2018
Chaurasia V, Pal S, Tiwari BB (2018) Prediction of benign and malignant breast cancer using data mining techniques
Chauhan P, Swami A, (2018) Breast Cancer Prediction Using Genetic Algorithm Based Ensemble Approach. 9th Int Conf Comput Commun Netw Technol, pp 1–8
Fu B, Liu P, Lin J, Deng L, Hu K, Zheng H (2018) Predicting Invasive Disease-Free Survival for Early-stage Breast Cancer Patients Using Follow-up Clinical Data. vol. 9294, no. c
Kaushik D (2018) Post-Surgical Survival forecasting of breast cancer patient : a novel approach. 2018 Int Conf Adv Comput Commun Informatics, pp 37–41
Ma M, Shi Y, Li W, Gao Y, Xu J (2018) A Novel Two-Stage Deep Method for Mitosis Detection in Breast Cancer Histology Images. In 2018 24th Int Conf Pattern Recognit, pp 3892–3897
Chang M, Dalpatadu RJ, Phanord D, Singh AK, Harrah WF (2018) Breast Cancer Prediction Using Bayesian Logistic Regression, vol. 2, pp 2–6
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Rahul, K., Banyal, R.K., Malik, V., Diksha (2021). Breast Cancer Prediction Using Enhanced CNN-Based Image Classification Mechanism. In: Singh Pundir, A.K., Yadav, A., Das, S. (eds) Recent Trends in Communication and Intelligent Systems. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-0167-5_3
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DOI: https://doi.org/10.1007/978-981-16-0167-5_3
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