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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 \u2013 1 learner or meta \u2013 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.<\/jats:p>","DOI":"10.3233\/jifs-201702","type":"journal-article","created":{"date-parts":[[2020,12,15]],"date-time":"2020-12-15T12:41:50Z","timestamp":1608036110000},"page":"4919-4934","source":"Crossref","is-referenced-by-count":8,"title":["Classification of Invasive Ductal Carcinoma from histopathology breast cancer images using Stacked Generalized Ensemble"],"prefix":"10.1177","volume":"40","author":[{"given":"Deepika","family":"Kumar","sequence":"first","affiliation":[{"name":"School of Engineering, GD Goenka University, Gurugram, Haryana, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Usha","family":"Batra","sequence":"additional","affiliation":[{"name":"School of Engineering, GD Goenka University, Gurugram, Haryana, 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