Breast cancer is one of the most common and lethal types of cancer worldwide, with millions of new cases diagnosed each year. Early detection is pivotal in improving patient outcomes and significantly increases the chances of successful treatment. While traditional detection methods such as mammography are effective, they can be invasive, costly, painful, and less applicable for younger women with denser breast tissue. In this context, infrared thermography emerges as a promising, non-invasive technique for breast cancer detection. However, analyzing these images presents challenges due to noise and irrelevant information that can interfere with accurate diagnosis. In this work, we propose a method for segmenting infrared breast images using the DeepLabV3+ Convolutional Neural Network (CNN). Our approach leverages the power of deep learning to precisely delineate breast regions, enabling more accurate feature extraction for subsequent classification tasks. Results achieved an average accuracy of 98.69%, an Intersection over Union (IoU) of 97.18%, and a precision of 98.48%, demonstrating a clear improvement over previous approaches, particularly in terms of segmentation quality, making our method a robust tool for enhancing automated breast cancer detection.

Hospital readmission is a complex health outcome, which burdens the patient, their family network, and the health system. The black box machine-learning techniques present challenges in validation, regulation, and understanding of health outcomes, such as hospital readmissions. Complex models based on deep learning generally achieve greater accuracy, although they generate tension between accuracy and interpretability. XGBoost outperforms traditional statistical methods and can potentially improve the prediction of negative health outcomes. We conducted a retrospective cohort study with patients under 18 in a tertiary university hospital. We extracted demographic, clinical, and nutritional data from the electronic hospital system. We used extreme gradient boosting (XGBoost) to build a predictive model for potentially avoidable 30-day readmissions. We use methods to calculate the importance scores of each variable for the generated model. Our study showed that it is possible to develop an interpretable prediction model for potentially preventable pediatric readmissions using the XGBoost algorithm.

Summary Wild boar ( Sus scrofa ) is a destructive species of swine. They spread diseases, represent a threat to native species, and destroy natural habitats by destabilizing river banks, thus reducing water flow. The monitoring of populations of wild boars is central to the execution and evaluation of methods to control them. To address this issue, in this article, we retrain and apply four convolutional neural networks (CNNs; AlexNet, VGG‐16, Inception‐v3, and ResNet‐50) to classify different species of “bush pigs” in real‐world footage: two native species of the Brazilian fauna, collared peccary ( Pecari tajacu ) and white‐lipped peccary ( Tayassu pecari ), and one invasive species, wild boar ( S. scrofa ). Results show that CNN can be used to classify animals with very similar behavior and appearance and that ResNet‐50 outperforms all compared CNN in terms of accuracy (98.33%) and the lowest probability of false positives (i.e., native species classified as wild boar).

Shape analysis is a very important field in computer vision. This work presents a novel and highly discriminative shape analysis method based on the weights of a Randomized Neural Network (RNN). Two approaches are proposed to extract the contour signature: Neighborhood approach uses the distance of each contour pixel and its immediate neighboring pixels and Contour portion approach, which uses metrics computed from contour sections to model the shape as RNN. We also proposed a signature that combines the feature vectors resulting from both approaches, thus resulting in a set of features tolerant to affine transformations, such as rotation and scale. We compared our approach with other shape analysis methods in 6 different shapes datasets. We calculated the accuracy as measure performance and obtained 97.98%, 99.07%, 84.67%, 87.67%, 88.92% and 80.58% for Kimia, Fish, Leaf, Rotated Leaf, Scaled Leaf and Noised Leaf datasets, respectively. The achieved performance of our method surpassed the results of several compared methods in most of these datasets, thus proving that our proposed signature can be applied successfully in shape analysis problems.

We study the problem of visualization of clusters in an educational data set based on convex-hull shape preservation algorithm. This problem considers multidimensional data with pre-established classes with the requirement of elements of different classes must be presented at distinctive regions. Such problem is commonly found on economic and social data, where visualization is important to understand a phenomenon before further analysis. In this paper, we propose an algorithm that uses a nonlinear transformation to preserve some data distance properties and display in a convenient format to interpretation. The proposed visualization algorithm is a partition-conforming projection, as defined by Kleinberg [An impossibility theorem for clustering, Adv. Neural Inform. Processing Syst. 15: Proc. 2002 Conf., 2003, The MIT Press, p. 463.], and completely separates the convex hull of data classes by applying locally linear operations. We applied this algorithm to visualize data from an important exam applied for over four million students of the Brazilian educational system Exame Nacional do Ensino Médio (ENEM). Results show that the proposed algorithm successfully separates unintelligible data and presents it more accessible to further visual analysis.

This work presents a novel approach for texture based image description, by representing an image as a complex network and applying an enhanced deterministic partially self-avoiding walks algorithm on a transformed image generated from the network's statistics, focusing on extraction of walk shape to build a feature vector to competently characterize texture. A significant improvement to the deterministic walks method proposed in this work is to create a complex network from an image and perform walks on the network's nodes' degrees, instead of on the intensity of the original image's pixels, as usual. Another meaningful innovation is in the way information is acquired from the walks: instead of using walk sizes or demanding fractal dimension computations, the proposed method derives shape information from a walk direction histogram. Experiments using the proposed method for texture classification on several widespread datasets show that the proposed method not only manages to reduce feature vector size but also improves correct classification rates compared to other state-of-the-art methods.

Classification of pap-smear cell images is a relevant and challenging medical problem. In order to contribute to the analysis of these images, we propose to compute complexity based features from each color channel of them. Moreover, we propose to use the gravitational method, a novel and very discriminative approach which improves our ability to extract descriptors from the images. We compared our approach with results obtained from other color texture analysis methods by using both accuracy and AUC, which are measurements of performance. The obtained results (89.64% for accuracy and 0.9086 for AUC) demonstrate that color texture classification using the gravitational model is a feasible approach for the classification of pap-smear images.

Color textures are among the most important visual attributes in image analysis. This paper presents a novel method to analyze color textures by modeling a color image as a graph in two different and complementary manners (each color channel separately and the three color channels altogether) and by obtaining statistical moments from the shortest paths between specific vertices of this graph. Such an approach allows to create a set of feature vectors, which were extracted from VisTex, USPTex, and TC00013 color texture databases. The best classification results were 99.07%, 96.85%, and 91.54% (LDA with leave-one-out), 87.62%, 66.71%, and 88.06% (1NN with holdout), and 98.62%, 96.16%, and 91.34% (LDA with holdout) of success rate (percentage of samples correctly classified) for these three databases, respectively. These results prove that the proposed approach is a powerful tool for color texture analysis to be explored.

Texture is an important visual attribute used to describe the pixel organization in an image. As well as it being easily identified by humans, its analysis process demands a high level of sophistication and computer complexity. This paper presents a novel approach for texture analysis, based on analyzing the complexity of the surface generated from a texture, in order to describe and characterize it. The proposed method produces a texture signature which is able to efficiently characterize different texture classes. The paper also illustrates a novel method performance on an experiment using texture images of leaves. Leaf identification is a difficult and complex task due to the nature of plants, which presents a huge pattern variation. The high classification rate yielded shows the potential of the method, improving on traditional texture techniques, such as Gabor filters and Fourier analysis.