Improved Gastrointestinal Screening: Deep Features using Stacked Generalization

Gastric malignancy – one of the five most deadliest types of cancer – exceeds annual cases by a million worldwide since 2017. Automated screening tools may help speed up the screening and clinical procedures. In this paper, we propose a binary classification approach to classify gastrointestinal cancer tissues, namely Microsatellite Instable (MSI) and Microsatellite Stable (MSS) through stacked generalization based ensemble Deep Neural Network (DNN)11Authors contributed equally to the work.. Using a dataset of size 192, 315 images, we achieve an overall accuracy of 94.91% and sensitivity of 95.95%. Our results outperform previous works.