Improved Gastrointestinal Screening: Deep Features using Stacked Generalization
Sourodip Ghosh, KC Santosh · 2021
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). 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.