Please use this identifier to cite or link to this item: https://hdl.handle.net/1822/78810

TitleDetection of bladder cancer with feature fusion, transfer learning and CapsNets
Author(s)Freitas, Nuno Renato Azevedo
Vieira, Pedro Miguel
Cordeiro, Agostinho
Tinoco, Catarina
Morais, Nuno
Torres, João Nuno Braga Pimentel
Anacleto, Sara
Laguna, M. Pilar
Lima, Estêvão Augusto Rodrigues de
Lima, C. S.
KeywordsBladder tumor
Capsule neural networks
Decision fusion
Ensemble learning
Transfer learning
Issue date2022
PublisherElsevier
JournalArtificial Intelligence in Medicine
Abstract(s)This paper confronts two approaches to classify bladder lesions shown in white light cystoscopy images when using small datasets: the classical one, where handcrafted-based features feed pattern recognition systems and the modern deep learning-based (DL) approach. In between, there are alternative DL models that had not received wide attention from the scientific community, even though they can be more appropriate for small datasets such as the human brain motivated capsule neural networks (CapsNets). However, CapsNets have not yet matured hence presenting lower performances than the most classic DL models. These models require higher computational resources, more computational skills from the physician and are more prone to overfitting, making them sometimes prohibitive in the routine of clinical practice. This paper shows that carefully handcrafted features used with more robust models can reach similar performances to the conventional DL-based models and deep CapsNets, making them more useful for clinical applications. Concerning feature extraction, it is proposed a new feature fusion approach for Ta and T1 bladder tumor detection by using decision fusion from multiple classifiers in a scheme known as stacking of classifiers. Three Neural Networks perform classification on three different feature sets, namely: Covariance of Color Histogram of Oriented Gradients, proposed in the ambit of this paper; Local Binary Patterns and Wavelet Coefficients taken from lower scales. Data diversity is ensured by a fourth Neural Network, which is used for decision fusion by combining the outputs of the ensemble elements to produce the classifier output. Both Feed Forward Neural Networks and Radial Basis Functions are used in the experiments. Contrarily, DLbased models extract automatically the best features at the cost of requiring huge amounts of training data, which in turn can be alleviated by using the Transfer Learning (TL) strategy. In this paper VGG16 and ResNet-34 pretrained in Imagenet were used for TL, slightly outperforming the proposed ensemble. CapsNets may overcome CNNs given their ability to deal with objects rotational invariance and spatial relationships. Therefore, they can be trained from scratch in applications using small amounts of data, which was beneficial for the current case, improving accuracy from 94.6% to 96.9%.
TypeArticle
URIhttps://hdl.handle.net/1822/78810
DOI10.1016/j.artmed.2022.102275
ISSN0933-3657
e-ISSN1873-2860
Publisher versionhttps://www.sciencedirect.com/science/article/pii/S0933365722000409
Peer-Reviewedyes
AccessRestricted access (UMinho)
Appears in Collections:ICVS - Artigos em revistas internacionais / Papers in international journals
CMEMS - Artigos em revistas internacionais/Papers in international journals

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