With a mortality rate of approximately 33.33%, Colorectal cancer serves as the second most prevalent malignant tumor type in the world. AI-guided clinical care/tool can help in reducing health disparities, specifically in resource-constrained regions. In this paper, using multi-class tissue features, we proposed an Ensemble Deep Neural Network to Tumor in Colorectal Histology images. On two different publicly available datasets: NCT-CRC-HE-100K (107,180 images) and Colorectal Histology (5000 images), we achieved accuracies of 96.16% and 92.83%, respectively. When datasets are combined, it provided a benchmark accuracy of 99.13%. We efficiently used resourced data, thereby achieving results that outperformed the state-of-the-art works.

结直肠癌的死亡率约为33.33％，是世界上第二大最普遍的恶性肿瘤类型。AI指导的临床护理/工具可以帮助减少健康差异，特别是在资源有限的地区。在本文中，我们使用多类组织特征，提出了一种在结肠直肠组织学图像中用于肿瘤的Ensemble深度神经网络。在两个不同的公开可用数据集：NCT-CRC-HE-100K（107,180张图像）和结肠直肠组织学（5000张图像）上，我们分别获得了96.16％和92.83％的准确性。合并数据集后，基准精度为99.13％。我们有效地利用了资源数据，从而获得了超越最新技术成果的结果。