Colorectal Histology Tumor Detection Using Ensemble Deep Neural Network. (April 2021)

• Record Type: Journal Article
• Authors: Ghosh, Sourodip; Bandyopadhyay, Ahana; Sahay, Shreya; Ghosh, Richik; Kundu, Ishita; Santosh, K.C.
• Is Part Of: Engineering applications of artificial intelligence. Volume 100(2021)
• Publication Date: 2021-04
• Subjects: Colorectal histology -- Tumor -- Deep ensemble network -- Imaging; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2021.104202
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: 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.