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Proceedings Paper

Cell density features from histopathological images to differentiate non-small cell lung cancer subtypes
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Paper Abstract

Histopathological evaluation plays a crucial role in the process of understanding lung cancer biology. Such evaluation consists in analyzing patterns related with tissue structure and cell morphology to identify the presence of cancer and the associated subtype. This investigation presents a multi-level texture approach to differentiate the two main lung cancer subtypes, adenocarcinoma (ADC) and squamous cell carcinoma (SCC), by estimating global spatial patterns in terms of cell density. Such patterns correspond to texture features computed from cell density distribution in a co-occurrence frame. Results using the proposed approach achieved an accuracy of 0.72 and F-score of 0.72.

Paper Details

Date Published: 3 January 2020
PDF: 6 pages
Proc. SPIE 11330, 15th International Symposium on Medical Information Processing and Analysis, 1133007 (3 January 2020); doi: 10.1117/12.2542360
Show Author Affiliations
Alvaro Andrés Sandino, Univ. Nacional de Colombia (Colombia)
Charlems Alvarez-Jimenez, Univ. Nacional de Colombia (Colombia)
Case Western Reserve Univ. (United States)
Andres Mosquera-Zamudio, Univ. Nacional de Colombia (Colombia)
Satish E. Viswanath, Case Western Reserve Univ. (United States)
Eduardo Romero, Univ. Nacional de Colombia (Colombia)


Published in SPIE Proceedings Vol. 11330:
15th International Symposium on Medical Information Processing and Analysis
Eduardo Romero; Natasha Lepore; Jorge Brieva, Editor(s)

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