Abstract
Growing obesity has been a worldwide issue for several years. This is the outcome of common nutritional disorders which results in obese individuals who are prone to many diseases. Managing diet while simultaneously dealing with the obligations of a working adult can be difficult. Today, people have a very fast-paced life and sometimes neglect food choices. In order to simplify the interpretation of the Nutri-score labeling this paper proposes a method capable of automatically reading food labels with this format. This method is intended to support users when choosing the products to buy based on the letter identification of the label. For this purpose, a dataset was created, and a prototype mobile application was developed using a deep learning network to recognize the Nutri-score information. Although the final solution is still in progress, the reading module, which includes the proposed method, achieved an encouraging and promising accuracy (above 90%). The upcoming developments of the model include information to the user about the nutritional value of the analyzed product combining it's Nutri-score label and composition.

Abstract
This paper presents a study on the automated detection of landmarks in medical x-ray images using deep learning techniques. In this work we developed two neural networks based on semantic segmentation to automatically detect landmarks in x-ray images, using a dataset of 200 encephalogram images: the UNet architecture and the FPN architecture. The UNet and FPN architectures are compared and it can be concluded that the FPN model, with IoU=0.91, is more robust and accurate in predicting landmarks. The study also had the goal of direct application in a medical context of diagnosing the models and their predictions. Our research team also developed a metric analysis, based on the encephalograms in the dataset, on the type of Mandibular Occlusion of the patients, thus allowing a fast and accurate response in the identification and classification of a diagnosis. The paper highlights the potential of deep learning for automating the detection of anatomical landmarks in medical imaging, which can save time, improve diagnostic accuracy, and facilitate treatment planning. We hope to develop a universal model in the future, capable of evaluating any type of metric using image segmentation.

Abstract
The determination of grape quality parameters is intricately linked to the mineral composition of the fruit; this relationship is increasingly affected by the impacts of climate change. The conventional chemical methodologies employed for the mineral quantification of grape tissues are expensive and impracticable for widespread commercial applications. This paper utilized Laser-Induced Breakdown Spectroscopy (LIBS) to analyze the mineral constituents within the skin, pulp, and seeds of two distinct Vitis vinifera cultivars: a white cultivar (Loureiro) and a red cultivar (Vinh & atilde;o). The primary objective was to discriminate the potential variations in the calcium (Ca), magnesium (Mg), and nitrogen (N) concentrations and water content among different grape tissues, explaining their consequential impact on the metabolic constitution of the grapes and, by extension, their influence on various quality parameters. Additionally, the study compared the mineral contents of the white and red grape cultivars across three distinct time points post veraison. Significant differences (p < 0.05) were observed between the Loureiro and Vinh & atilde;o cultivars in Ca concentrations across all the dates and tissues and for Mg in the skin and pulp, N in the pulp and seeds, and water content in the skin and pulp. In the Vinh & atilde;o cultivar, Ca differences were found in the pulp across the dates, N in the seeds, and water content in the skin, pulp, and seeds. Comparing the cultivars within tissues, Ca exhibited differences in the pulp, Mg in the skin and pulp, N in the pulp and seeds, and water content in the skin, pulp, and seeds. These findings provide insights into the relationship between the grape mineral and water content, climatic factors, and viticulture practices within a changing climate.

Abstract
Enlarged excavation of the optic papilla, caused by the loss of fibres that originate in the retina and transmit electrical stimuli to the visual cortex, is a critical indicator in the early detection of glaucoma, a disease that can lead to irreversible blindness. As the optic papilla shows morphological variations in the population, its identification can be a challenge. Methods based on deep learning have shown promise in helping doctors analyse these images more accurately. Recently, models such as Vision Transformers (ViT) have shown significant results in various medical applications, including glaucoma detection. However, the scarcity of quality data remains a major obstacle to training these models. This study evaluated the performance of the Swin Transformer, DeiT and Linformer models in detecting optic papilla excavation, using the new Brazilian Multilabel Ophthalmological Dataset (BRSET). The results showed that the DeiT model obtained the best accuracy, with 0.94, followed by the Swin Transformer, with 0.88, and the Linformer, with 0.85. The findings of this study suggest that ViT models can not only significantly improve the detection of glaucomatous papillary excavation, but also strengthen Human-Machine Collaboration, promoting more effective interaction between doctors and automated systems in medical diagnosis.

Abstract
The medical imaging field contends with limited data for training deep learning (DL) models. Our study evaluated traditional data augmentation (DA) and Generative Adversarial Networks (GANs) in enhancing DL models for identifying stomach precancerous lesions. Classic DA consistently outperformed GAN-based methods with ResNet50 (0.94 vs 0.93 accuracy) and ViT (0.85 vs 0.84 accuracy) models achieving higher accuracy and other performance metrics with DA compared to GANs. Despite this, GAN augmentation showed significant improvements when compared to train with the original dataset, highlighting its role in diversifying datasets and aiding generalization across different medical imaging datasets. Combining both augmentation techniques can enhance model robustness and generalisation capabilities in DL applications for medical diagnostics, leveraging DA's consistency and GANs' diversity.

Abstract
Histopathological analysis is an essential exam for detecting various types of cancer. The process is traditionally time-consuming and laborious. Taking advantage of deep learning models, assisting the pathologist in the diagnosis process is possible. In this work, a study was carried out based on the DenseNet neural network. It consisted of changing its architecture through combinations of Transformer and MBConv blocks to investigate its impact on classifying histopathological images of penile cancer. Due to the limited number of samples in this dataset, pre-training is performed on another larger lung and colon cancer histopathological image dataset. Various combinations of these architectural components were systematically evaluated to compare their performance. The results indicate significant improvements in feature representation, demonstrating the effectiveness of these combined elements resulting in an F1-Score of up to 95.78%. Its diagnostic performance confirms the importance of deep learning techniques in men's health.