Deep Learning-Based Pixel-Wise Lesion Segmentation on Oral Squamous Cell Carcinoma Images

• Published in: Applied Sciences
• Main Authors: Francesco Martino, Domenico D. Bloisi, Andrea Pennisi, Mulham Fawakherji, Gennaro Ilardi, Daniela Russo, Daniele Nardi, Stefania Staibano, Francesco Merolla
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI AG 2020
• Subjects: oral carcinoma; medical image segmentation; deep learning; Technology; T; Engineering (General). Civil engineering (General); TA1-2040; Biology (General); QH301-705.5; Physics; QC1-999; Chemistry; QD1-999; Orca
• Online Access: https://doi.org/10.3390/app10228285; https://doaj.org/article/4b2ea031868c4139919d52fb2cf7e5d8

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.