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

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...

Full description

Bibliographic Details
Published in:Applied Sciences
Main Authors: Martino F., Bloisi D. D., Pennisi A., Fawakherji M., Ilardi G., Russo D., Nardi D., Staibano S., Merolla F.
Other Authors: Martino, F., Bloisi, D. D., Pennisi, A., Fawakherji, M., Ilardi, G., Russo, D., Nardi, D., Staibano, S., Merolla, F.
Format: Article in Journal/Newspaper
Language:English
Published: 2020
Subjects:
Deep learning
Medical image segmentation
Oral carcinoma
Orca
Online Access:https://hdl.handle.net/11695/98182
https://doi.org/10.3390/app10228285
Description
Summary: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.

Similar Items