October 6, 2017 Dataset Open Access

Sara Moccia; Elena De Momi; Leonardo S. Mattos

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    <subfield code="a">&lt;p&gt;The dataset in &lt;em&gt;&lt;strong&gt;laryngeal dataset.tar &lt;/strong&gt;&lt;/em&gt;contains 1320 patches of healthy and early-stage cancerous laryngeal tissues. The patches (100x100 pixels) were manually extracted from 33 narrow-band laryngoscopic images of 33 different patients affected by laryngeal spinocellular carcinoma (diagnosed after histopathological examination).&lt;/p&gt;

&lt;p&gt;Specifically, four tissue classes were considered (330 patches/tissue class): He (healthy tissue), Hbv (tissue with hypertrophic vessels), Le (tissue with leukoplakia) and IPCL (tissue with intrapapillary capillary loops).&lt;/p&gt;

&lt;p&gt;The dataset was created for testing the method proposed in &lt;em&gt;Moccia, Sara, et al. "Confident texture-based laryngeal tissue classification for early stage diagnosis support." JOURNAL OF MEDICAL IMAGING 4.03 (2017): 1-10.&lt;/em&gt;&lt;/p&gt;

&lt;p&gt;The folder&lt;em&gt; &lt;strong&gt;laryngeal dataset.tar&lt;/strong&gt; &lt;/em&gt;contains 3 subfolders (FOLD 1, FOLD 2, FOLD 3), which are the 3 folds used for cross-validation purpose in the tissue classification performance assessment.&lt;/p&gt;

&lt;p&gt;Each subfolder contains 4 folders relative to the four tissue classes, i.e., Le, He, Hbv, IPCL.&lt;/p&gt;

&lt;p&gt;----------------------------------------------------------------------------------------------------------------------------------------------------------&lt;/p&gt;

&lt;p&gt;If you want to use the dataset, please cite &lt;em&gt;Moccia, Sara, et al. "Confident texture-based laryngeal tissue classification for early stage diagnosis support." JOURNAL OF MEDICAL IMAGING 4.03 (2017): 1-10.&lt;/em&gt;&lt;/p&gt;

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