Automatic image segmentation using Region-Based convolutional networks for Melanoma skin cancer detection
Melanoma is one of the most aggressive skin cancers, however, its early detection can significantly increase probabilities to cure it. Unfortunately, it is one of the most difficult skin cancers to detect, its detection relies mainly on the dermatologist’s expertise and experience with Melanoma. Thi...
| Autores Principales: | Tovar-Parra, Karen Dayana, Calvo-Valverde, Luis Alexander, Montero-Zeledón, Ernesto, Murillo-Fernández, Mac Arturo, Perez-Hidalgo, Jose Esteban, Gutiérrez-Fallas, Dionisio Alberto |
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| Formato: | Artículo |
| Idioma: | Inglés Español |
| Publicado: |
Editorial Tecnológica de Costa Rica (entidad editora)
2022
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| Materias: | |
| Acceso en línea: |
https://revistas.tec.ac.cr/index.php/tec_marcha/article/view/6479 https://hdl.handle.net/2238/14161 |
| Sumario: |
Melanoma is one of the most aggressive skin cancers, however, its early detection can significantly increase probabilities to cure it. Unfortunately, it is one of the most difficult skin cancers to detect, its detection relies mainly on the dermatologist’s expertise and experience with Melanoma. This research deals with targeting most of the common Melanoma stains or spots that could potentially evolve to Melanoma skin cancer. Region-based Convolutional Neural Networks were used as the model to detect and segment images of the skin area of interest. The neural network model is focused on providing instance segmentation rather than only a boxbounding object detection. The Mask R-CNN model was implemented to provide a solution for small trained datasets scenarios. Two pipelines were implemented, the first one was with only the Region-Based Convolutional Neural Network and the other one was a combined pipeline with a first stage using Mask R-CNN and then getting the result to use as feedback in a second stage implementing Grabcut, which is another segmentation method based on graphic cuts. Results demonstrated through Dice Similarity Coefficient and Jaccard Index that Mask R-CNN alone performed better in proper segmentation than Mask R-CNN + Grabcut model. In both models’ results, variation was very small when the training dataset size changed between 160, 100, and 50 images. In both of the pipelines, the models were capable of running the segmentation correctly, which illustrates that focalization of the zone is possible with very small datasets and the potential use of automatic segmentation to assist in Melanoma detection. |
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