Affiliation:
1. School of Engineering, GD Goenka University, Gurugram, Haryana, India
Abstract
Breast cancer positions as the most well-known threat and the main source of malignant growth-related morbidity and mortality throughout the world. It is apical of all new cancer incidences analyzed among females. However, machine learning algorithms have given rise to progress across different domains. There are various diagnostic methods available for cancer detection. However, cancer detection through histopathological images is considered to be more accurate. In this research, we have proposed the Stacked Generalized Ensemble (SGE) approach for breast cancer classification into Invasive Ductal Carcinoma+ and Invasive Ductal Carcinoma-. SGE is inspired by the stacking model which utilizes output predictions. Here, SGE uses six deep learning models as level-0 learner models or sub-models and Logistic regression is used as Level – 1 learner or meta – learner model. Invasive Ductal Carcinoma dataset for histopathology images is used for experimentation. The results of the proposed methodology have been compared and analyzed with existing machine learning and deep learning methods. The results demonstrate that the proposed methodology performed exponentially good in image classification in terms of accuracy, precision, recall, and F1 measure.
Subject
Artificial Intelligence,General Engineering,Statistics and Probability
Reference47 articles.
1. Ferlay J. , Héry C. , Autier P. and Sankaranarayanan R. , Global burden of breast cancer, in Breast Cancer Epidemiology, Springer New York, 2010, pp. 1–19.
2. Zaidi Z. and Dib H.A. , Abstract: The worldwide female breast cancer incidence and survival, 2018 (2019), 4191–4191.
3. Incidence and mortality of female breast cancer in the Asia-Pacific region;Youlden;Cancer Biol Med,2014
4. Kumar D. and Batra U. , Epidemiology of breast cancer in indian women: Population and hospital based study, EAI Endorsed Trans Pervasive Heal Technol 4(16) (2018).
5. Automated breast cancer detection and classification using ultrasound images: A survey;Cheng;Pattern Recognit,2010
Cited by
7 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献