@inproceedings{
 title = {Cell density features from histopathological images to differentiate non-small cell lung cancer subtypes},
 type = {inproceedings},
 year = {2020},
 keywords = {Cell density,Classification,Co-occurrence matrix,Histopathology,Lung cancer,Texture features},
 volume = {11330},
 id = {a764c521-4b74-3e4e-8d55-227d5c3ea89d},
 created = {2023-10-25T08:56:39.135Z},
 file_attached = {false},
 profile_id = {eaba325f-653b-3ee2-b960-0abd5146933e},
 last_modified = {2023-10-25T08:56:39.135Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {false},
 hidden = {false},
 private_publication = {true},
 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.},
 bibtype = {inproceedings},
 author = {Sandino, A.A. and Alvarez-Jimenez, C. and Mosquera-Zamudio, A. and Viswanath, S.E. and Romero, E.},
 doi = {10.1117/12.2542360},
 booktitle = {Proceedings of SPIE - The International Society for Optical Engineering}
}

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