 The paper proposes a novel approach for automated detection of lung and colon cancer subtypes from histopathology images. The authors used a deep learning architecture based on the principles of compound scaling and progressive learning, fShintNet v2 large, medium, and small models. They achieved an accuracy of 99.97%, AUC of 99.99%, F1 score of 99.97%, balanced accuracy of 99.97%, and Matthew's correlation coefficient of 99.96%. Additionally, they generated visual saliency maps to precisely locate the vital regions in the histopathology images from the test set where the models put more attention during cancer subtype predictions. This visual saliency maps may potentially assist pathologists to design better treatment strategies. This article was authored by Sudhakar Tamala, Sifadine Kadri, Akman Nadeem, and others. We are article.tv, links in the description below.