Colorectal Histology Tumor Detection Using Ensemble Deep Neural Network

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...

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Bibliographic Details
Published inEngineering applications of artificial intelligence Vol. 100; p. 104202
Main Authors Ghosh, Sourodip, Bandyopadhyay, Ahana, Sahay, Shreya, Ghosh, Richik, Kundu, Ishita, Santosh, K.C.
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 01.04.2021
Subjects
Colorectal histology
Deep ensemble network
Imaging
Tumor
Deep ensemble network
Tumor
Imaging
Colorectal histology
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Summary: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.
ISSN:0952-1976
1873-6769
DOI:10.1016/j.engappai.2021.104202