Oral squamous cell carcinoma is the most common oral cancer. In this paper, we present a performance analysis of four different deep learning-based pixel-wise methods for lesion segmentation on oral carcinoma images. Two diverse image datasets, one for training and another one for testing, are used to generate and evaluate the models used for segmenting the images, thus allowing to assess the generalization capability of the considered deep network architectures. An important contribution of this work is the creation of the Oral Cancer Annotated (ORCA) dataset, containing ground-truth data derived from the well-known Cancer Genome Atlas (TCGA) dataset.

Deep learning-based pixel-wise lesion segmentation on oral squamous cell carcinoma images / Martino, F.; Bloisi, D. D.; Pennisi, A.; Fawakherji, M.; Ilardi, G.; Russo, D.; Nardi, D.; Staibano, S.; Merolla, F.. - In: APPLIED SCIENCES. - ISSN 2076-3417. - 10:22(2020), pp. 1-14. [10.3390/app10228285]

Deep learning-based pixel-wise lesion segmentation on oral squamous cell carcinoma images

Martino F.;Ilardi G.;Russo D.;Staibano S.;Merolla F.
2020

Abstract

Oral squamous cell carcinoma is the most common oral cancer. In this paper, we present a performance analysis of four different deep learning-based pixel-wise methods for lesion segmentation on oral carcinoma images. Two diverse image datasets, one for training and another one for testing, are used to generate and evaluate the models used for segmenting the images, thus allowing to assess the generalization capability of the considered deep network architectures. An important contribution of this work is the creation of the Oral Cancer Annotated (ORCA) dataset, containing ground-truth data derived from the well-known Cancer Genome Atlas (TCGA) dataset.
2020
Deep learning-based pixel-wise lesion segmentation on oral squamous cell carcinoma images / Martino, F.; Bloisi, D. D.; Pennisi, A.; Fawakherji, M.; Ilardi, G.; Russo, D.; Nardi, D.; Staibano, S.; Merolla, F.. - In: APPLIED SCIENCES. - ISSN 2076-3417. - 10:22(2020), pp. 1-14. [10.3390/app10228285]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/831763
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