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Rhaiu-net: precise tumor segmentation in breast ultrasound images

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dc.contributor.authorMartínez Gárriz, Iñaki
dc.contributor.directorDíez Vegas, Francisco Javier
dc.contributor.directorPérez Martín, Jorge
dc.contributor.directorRincón Zamorano, Mariano
dc.date.accessioned2024-05-20T12:38:30Z
dc.date.available2024-05-20T12:38:30Z
dc.date.issued2022-06-01
dc.description.abstractBreast cancer is the most common cancer among women. Moreover, according to the International Agency of Research on Cancer (IARC) breast cancer has already been the most diagnosed cancer in the world since 2021. The most determining factor when it comes to saving the life of a patient is its early detection. For this reason, computer-aided diagnosis (CAD) systems are being developed, so that breast cancer screening can be done faster and more accurately. Ultrasound is one of the most used screening techniques because it is inexpensive, fast, radiation-free, and painless. However, breast ultrasound image segmentation is a challenging task since they usually are speckle noisy and they suffer from low contrast. For this reason, the task is very time-consuming and needs to be performed by specialized doctors. The current trend in breast ultrasound image segmentation is building deep learning models, specifically; convolutional neural networks (CNNs), which are reporting very promising performance. In this work, we propose RHAIU-Net, a CNN-based novel architecture for breast ultrasound image segmentation. Specifically, it is based on the U-Net architecture, incorporating residual inception blocks, SiLU activation functions, attention gates, and Hartley pooling layers. We also propose a training framework which consists of data augmentation, image prepossessing, learning rate decay, and adversarial training. We have trained it along with other 11 models based on state-of-the-art architectures with the same dataset and framework, and then we have compared them. RHAIU-Net has outperformed nearly all of them in small tumors while performing as well as others in bigger ones.en
dc.description.versionversión final
dc.identifier.urihttps://hdl.handle.net/20.500.14468/14659
dc.language.isoen
dc.publisherUniversidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Inteligencia Artificial
dc.relation.centerE.T.S. de Ingeniería Informática
dc.relation.degreeMáster universitario en Investigación en Inteligencia Artificial
dc.relation.departmentInteligencia Artificial
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
dc.subject.keywordsRHAIU-Net
dc.subject.keywordsBreast ultrasound (BUS)
dc.subject.keywordsBreast tumor
dc.subject.keywordsTumor segmentation
dc.subject.keywordsConvolutional neural network
dc.titleRhaiu-net: precise tumor segmentation in breast ultrasound imageses
dc.typetesis de maestríaes
dc.typemaster thesisen
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