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Title: Detección del carcinoma de glándula mamaria mediante termografía infrarroja usando redes neuronales profundas
Author: Fernández Ovies, Francisco Javier
Director: Prados Carrasco, Ferran
Tutor: Alférez Baquero, Edwin Santiago
Keywords: breast cancer
deep neuronal network
Issue Date: 2-Jan-2019
Publisher: Universitat Oberta de Catalunya (UOC)
Abstract: The main objective of this work is the evaluation of a convolutional neuronal network that facilitates the early detection of breast cancer using infrared thermography images. The literature shows satisfactory results in previous studies using other algorithms. The main challenges of using neural networks are two, having a large number of images and processing time. The approach is to use the high-level functions implemented in the library, on the Pytorch platform, which have obtained excellent results in the image classification, as endorsed by their successes in the Kaggle competitions, a world reference in technologies Machine Learning. The images are organized in the three usual groups: training, validation and testing; what has told us to contrast the results using different pre-trained architectures (renet18, resnet34, resnet50, resnet152, vgg16 and vgg19) use the processing time, assess their classification by means of confusion matrices, obtain the best result with resnet34, with a accuracy in the 0.985 test.
Language: Spanish
Appears in Collections:Bachelor thesis, research projects, etc.

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