Loading...
6 results
Search Results
Now showing 1 - 6 of 6
- Evaluating Intelligent Methods for Decision Making Support in Dermoscopy Based on Information Gain and EnsemblePublication . Spolaôr, Newton; Fonseca-Pinto, Rui; Mendes, Ana I.; Ensina, Leandro A.; Takaki, Weber S. R.; Parmezan, Antonio R. S.; Nogueira, Conceição V.; Coy, Claudio S. R.; Wu, Feng C.; Lee, Huei D.Melanoma, the most dangerous skin cancer, is sometimes associated with a nevus, a relatively common skin lesion. To find early melanoma, nevus, and other lesions, dermoscopy is often used. In this context, intelligent methods have been applied in dermoscopic images to support decision making. A typical computer-aided diagnosis method comprises three steps: (1) extraction of features that describe image properties, (2) selection of important features previously extracted, (3) classification of images based on the selected features. In this work, traditional data mining approaches underexploited in dermoscopy were applied: information gain for feature selection and an ensemble classification method based on gradient boosting. The former technique ranks image features according to data entropy, while the latter combines the outputs of single classifiers to predict the image class. After evaluating these approaches in a public dataset, we can observe that the results obtained are competitive with the state-of-the-art. Moreover, the presented approach allows a reduction of the total number of features and types of features to produce similar classification scores.
- Complete reducibility of the pseudovariety LS1Publication . Costa, José Carlos; Nogueira, Conceição; Nogueira, ConceiçãoIn this paper we prove that the pseudovariety LSl of local semilattices is completely κ-reducible, where κ is the implicit signature consisting of the multiplication and the ω-power. Informally speaking, given a finite equation system with rational constraints, the existence of a solution by pseudowords of the system over LSl implies the existence of a solution by κ-words of the system over LSl satisfying the same constraints.
- Fine-tuning pre-trained neural networks for medical image classification in small clinical datasetsPublication . Spolaôr, Newton; Lee, Huei Diana; Mendes, Ana Isabel; Nogueira, Conceição; Parmezan, Antonio Rafael Sabino; Takaki, Weber Shoity Resende; Coy, Claudio Saddy Rodrigues; Wu, Feng Chung; Fonseca-Pinto, RuiConvolutional neural networks have been effective in several applications, arising as a promising supporting tool in a relevant Dermatology problem: skin cancer diagnosis. However, generalizing well can be difficult when little training data is available. The fine-tuning transfer learning strategy has been employed to differentiate properly malignant from non-malignant lesions in dermoscopic images. Fine-tuning a pre-trained network allows one to classify data in the target domain, occasionally with few images, using knowledge acquired in another domain. This work proposes eight fine-tuning settings based on convolutional networks previously trained on ImageNet that can be employed mainly in limited data samples to reduce overfitting risk. They differ on the architecture, the learning rate and the number of unfrozen layer blocks. We evaluated the settings in two public datasets with 104 and 200 dermoscopic images. By finding competitive configurations in small datasets, this paper illustrates that deep learning can be effective if one has only a few dozen malignant and non-malignant lesion images to study and differentiate in Dermatology. The proposal is also flexible and potentially useful for other domains. In fact, it performed satisfactorily in an assessment conducted in a larger dataset with 746 computerized tomographic images associated with the coronavirus disease.
- On the geometric modulation of skin lesion growth: a mathematical model for melanomaPublication . Mendes, Ana Isabel; Nogueira, Conceição; Pereira, Jorge; Fonseca-Pinto, RuiIntroduction: Early detection of suspicious skin lesions is critical to prevent skin malignancies, particularly the melanoma, which is the most dangerous form of human skin cancer. In the last decade, image processing techniques have been an increasingly important tool for early detection and mathematical models play a relevant role in mapping the progression of lesions. Methods: This work presents an algorithm to describe the evolution of the border of the skin lesion based on two main measurable markers: the symmetry and the geometric growth path of the lesion. The proposed methodology involves two dermoscopic images of the same melanocytic lesion obtained at different moments in time. By applying a mathematical model based on planar linear transformations, measurable parameters related to symmetry and growth are extracted. Results: With this information one may compare the actual evolution in the lesion with the outcomes from the geometric model. First, this method was tested on predefined images whose growth was controlled and the symmetry known which were used for validation. Then the methodology was tested in real dermoscopic melanoma images in which the parameters of the mathematical model revealed symmetry and growth rates consistent with a typical melanoma behavior. Conclusions: The method developed proved to show very accurate information about the target growth markers (variation on the growth along the border, the deformation and the symmetry of the lesion trough the time). All the results, validated by the expected phantom outputs, were similar to the ones on the real images.
- Semigroup presentations for test local groupsPublication . Costa, J. C.; Nogueira, C.; Teixeira, M. L.In this paper we exhibit a type of semigroup presentation which determines a class of local groups. We show that the finite elements of this class generate the pseudovariety LG of all finite local groups and use them as test-semigroups to prove that LG and S, the pseudovariety of all finite semigroups, verify the same $κ-identities involving κ-terms of rank at most 1, where κ denotes the implicit signature consisting of the multiplication and the (ω-1)-power.
- Pointlike reducibility of pseudovarieties of the form V ∗DPublication . Costa, José Carlos; Nogueira, Conceição; Teixeira, Maria LurdesIn this paper, we investigate the reducibility property of semidirect products of the form [Formula: see text] relatively to (pointlike) systems of equations of the form [Formula: see text], where [Formula: see text] d̃enotes the pseudovariety of definite semigroups. We establish a connection between pointlike reducibility of [Formula: see text] and the pointlike reducibility of the pseudovariety [Formula: see text]. In particular, for the canonical signature [Formula: see text] consisting of the multiplication and the [Formula: see text]-power, we show that [Formula: see text] is pointlike [Formula: see text]-reducible when [Formula: see text] is pointlike [Formula: see text]-reducible.