Tuberculosis (TB) remains a major global health crisis, disproportionately affecting vulnerable populations. Despite advances in artificial intelligence (AI) for chest X-ray (CXR) analysis, these tools have limited impact in low-resource regions due to inadequate infrastructure, specialist shortages, and high equipment costs. This study proposes a practical AI-based approach using optimized binary phase pattern congruence (BPPC) feature selection to distinguish between TB cases and healthy individuals. Its lower computational requirements and costs make it particularly suitable for vulnerable regions. We experimented with multiple CXR databases and segmentation scenarios using optimized feature selection. Results outperform existing literature, achieving a minimum area under the curve (AUC) of 97.64%, showing potential to enhance CXR analysis and assist specialists in TB diagnosis.

Depth estimation plays a crucial role in understanding spatial relationships within natural scenes, enabling applications in 3D modeling, robotics, and environmental monitoring. This paper presents a comparative study between the Depth Anything model—a monocular depth estimation framework—and LiDAR sensors in forest environments. A real-world dataset of 4,613 frames captured from a eucalyptus farm in Açailândia, Maranhão, Brazil, was used for evaluation. Depth Anything was trained using a large-scale dataset with 1.5 million labeled and 62 million unlabeled images. The error metrics used in this study include MAE (Mean Absolute Error) and RMSE (Root Mean Squared Error), achieving total values of 0.1096 meters and 0.1328 meters, respectively. These results demonstrate high alignment with LiDAR measurements and robustness in complex environments. Furthermore, the analysis includes a per-tree evaluation and statistical distribution through boxplots, confirming stable and consistent predictions. This study suggests the potential of combining monocular models with traditional sensors to enhance depth estimation for forest management and biodiversity monitoring.

Tuberculosis is a severe and contagious lung dis-ease that kills about 1.5 million people worldwide. One of the ways to combat this disease is by tracking, detecting, and iso-lating the infected. In this sense, chest radiography (CXR) is an effective alternative for this task, given its high availability, low charge, and quick response. Thus, considering the importance of this topic, our work proposal is a machine learning method for tuberculosis detection in CRXs. Our method combines local binary patterns (LBP) feature extraction and a feature selection wrapper algorithm by Monarch Butterfly Optimization (MBO) with an evaluation KNN classifier. The results are compared to a reference work on various metrics and show 90.33 % and 92.41 % accuracy and the area under the ROC curve, respectively. Our proposal is a solution that combines performance, reduced computational cost, and simplicity of implementation, composing a viable and aligned alternative to the Internet of Things (IoT) solutions.

Since vaccination started, the COVID-19 scenario has improved. On the other hand, although the number of deaths has significantly dropped, the number of new cases is still a concern. Thus, patient tracking and follow-up are essential tasks, and chest X-ray examination is the first-order tool. While several studies using CXR and computing have been developed, they did not translate into clinical applications yet. One of the reasons is the computational effort required to run huge deep learning models and its high cost to be adopted in community clinics. Therefore, this work proposes a lightweight (few computational resources needed), fast (training and inference time), and reasoned solution for automatic COVID-19 detection and assessment of its severity. Our method is based on extracting features by Binary Pattern of Phase Congruency (BPPC) in segmented CXR images. Radiomic features are extracted from the segmented CXR image, and an SVM-based selection process is used to build two models of a shallow Feed-Forward network. The results surpass previous studies, with an average accuracy for COVID-19 detection of 98.71%. For images without evidence of infection but with a positive PCR test, an accuracy of 94.74% is reached. In a second task, the severity level of COVID 19 is estimated with an AUC of 98.92%. This high performance helps improve the speed and accuracy of diagnosis and severity assessment of COVID19 infection, proving to be a viable option in transitioning from a research field to a clinical environment.

Amyotrophic Lateral Sclerosis (ALS) is a neurode-generative disease characterized by the progressive and specific loss of motor neurons in the brain, causing a variety of symptoms, including weakness of muscles and changes in gait. Currently, there is no cure for ALS, nor there is a definitive diagnostic test that can detect whether someone has ALS. Therefore, there is still a need for alternative and non-invasive methods to aid the diagnosis of ALS. This article proposes an automatic method to aid the diagnosis of ALS. A feature extraction technique based on metrics of fluctuation magnitude and fluctuation dynamics, followed by a machine learning algorithm to separate subjects with ALS from healthy ones, was used. The results showed that the proposed approach is comparable to others in the literature even though a simpler and smaller feature set was considered. Five different machine learning classifiers were compared and evaluated using the leave-one-out cross-validation method. A comparison and discussion of the results based on the foot from which the data were extracted and the phases of the gait were also carried out.

Governments, civil society, health professionals, and scientists have been facing a relentless fight against the pandemic of the COVID-19 disease; however, there are already about 150 million people infected worldwide and more than 3 million lives claimed, and numbers keep rising. One of the ways to combat this disease is the effective screening of infected patients. However, COVID-19 provides a similar pattern with diseases, such as pneumonia, and can misguide even very well-trained physicians. In this sense, a chest X-ray (CXR) is an effective alternative due to its low cost, accessibility, and quick response. Thus, inspired by research on the use of CXR for the diagnosis of COVID-19 pneumonia, we investigate classical machine learning methods to assist in this task. The main goal of this work is to present a robust, lightweight, and fast technique for the automatic detection of COVID-19 from CXR images. We extracted radiomic features from CXR images and trained classical machine learning models for two different classification schemes: i) COVID-19 pneumonia vs. Normal ii) COVID-19 vs. Normal vs. Viral pneumonia. Several evaluation metrics were used and comparison with many studies is presented. Our experimental results are equivalent to the state-of-the-art for both classification schemes. The solution’s high performance makes it a viable option as a computer-aided diagnostic tool, which can represent a significant gain in the speed and accuracy of the COVID-19 diagnosis.

In methodologies that make use of the remote sensing images obtained by orbital sensors, it is very common the application of resampling methods with the adaptation of images contained bands with different spatial resolutions, for example, the Sentinel-2 sensor, with thirteen bands, four with a resolution of 10m, six of 20m and three with 60m. In this way, to calculate some vegetation indices, the difference of spatial resolution among bands does not allow index calculation, requiring the application of resampling. In the literature, there are several techniques, but the effects derived from that pixel transformation have not been explored much when related to sugarcane classification. Thus, this paper applies different resampling methodologies focused on remote sensing, verifying the effects of each transformation in the vegetation indices calculation to perform sugarcane varieties discrimination. It was possible to observe little variation in accuracy amid the applied methods, showing little influence in the process to identify sugarcane varieties. Thus, the use of indices related to chlorophyll demonstrated great potential for the purpose of discriminating/classifying sugarcane, presenting alternative vegetation indices to be applied for this type of purpose.

Management of agricultural production and rural activities has been supported by recognizing machine learning patterns and algorithms, as in the automation of leaf analysis. However, leaf border damage compromises leaf structures, making it difficult to estimate the lost contours. Effects caused by predatory insects are difficult to be monitored by inspection processes, and the harmful results caused by them can deteriorate the performance of machine learning models. In this sense, plant leaves that are not fresh or intact are avoided. Consequently, the number of samples for use in training steps is reduced, leading to problems of data balancing and limited generalization models. This study presents an automatic method for reconstructing an injured leaf at a probable stage before defoliation. Thus, the reconstruction of damaged leaves can be used to maximize the number of samples in the plant species classification processes and provide visible results for the agronomic analysis of regions of occurrence of leaf damage and the components of the primary leaf structure affected by predatory insects. Based on the experimental results, we conclude that the proposed approach can accurately delimit the injured leaf silhouette and restore the leaf regions affected by herbivory attacks.

Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease that affects the nervous system responsible for muscle movement and eventually compromising one's ability to walk. Diagnosing ALS is a difficult task since no test can provide a definite diagnosis. In this sense, automatic methods that aid the diagnosis of ALS have an essential role in helping to reach a diagnose. However, most of the existing approaches that use gait dynamics are based on a 5-minute observation, which can be exhausting and demanding for a patient with ALS seeking the diagnosis. This paper proposes an automated method to aid the diagnosis of ALS using information obtained from one minute gait observation. The GaitNDD database, which provides gait data recorded for 5 minutes from people with ALS and from healthy subjects, was used to support and validate this study. Results are reported and evaluated for different machine learning classifiers. Features extracted from either 1-min or 5-min observations are evaluated. Our results show that 96.6% of accuracy was achieved for data derived from either the first or the 5-minute walking, with excellent sensitivity and specificity, thus showing that our method can help aid the diagnosis of ALS while reducing the time required for the walking experiment.

Chest radiography (CXR) is one of the first choices in epidemiological analyses such as tuberculosis, cancer, pneumonia, and, recently, COVID-19. It provides crucial information for decision making, treatment, and monitoring the evolution of clinical cases from small to high complexity. Thus, it is a valuable source of information for the study, training, research, and development of computational support to medical diagnoses. In this work, we introduce a new method for chest X-ray adjustment to identifying and correcting radiographic images orientation. So, they can be automatically rotated to a standard position. Our proposal uses structural characteristics and statistics of pixel intensity patterns of CXR images. Divided into three steps, our method begins with the preparation of the photos, followed by a feature extraction strategy, and it ends with the X-ray image orientation identification. We use three different databases that include pediatric and adult radiographic imaging. A result showed 99.4% accuracy in the databases in our experiments. The code prepared by the authors is publicly available.

A disparity map is a key component of stereo vision systems. Autonomous navigation, 3D reconstruction and mobility are examples of areas that use disparity maps as an important element. Although much work has been done in the stereo vision field, it is not easy to build stereo systems with concepts such as reuse and extensible scope. In the present paper, we contribute to reducing this gap by presenting a software architecture that can accommodate different stereo methods through a new standard structure. Firstly, we introduce scenarios that illustrate use cases of disparity maps, and we show a novel architecture that foments code reuse. A Disparity Computation Framework (DCF) is presented and how its components are structured regarding compartmentalization are discussed. Then, we introduce a prototype that closely follows our proposal, and we describe some test cases that were performed. We conclude that the DCF can satisfy different on-demand scenarios and that it can support new stereo methods, functions, and evaluations for different applications without much effort.

Huntington's Disease (HD) is a genetic disorder that causes the progressive breakdown of nerve cells in the brain, reducing an individual's ability to reason, walk, and speak. Due to its severity, new approaches are important for the development of methods that contribute to the correct classification of this disease. In this paper, we propose an automatic method for diagnosing Huntington's Disease using gait dynamics information. Our approach is divided into a four-stage pipeline: preprocessing, feature extraction, classification, and diagnosis output. We evaluate the performance of our proposed method through well-known classifiers that are commonly used in machine learning problems. A publicly available database on Gait Dynamics in Neuro-Degenerative Disease is used, and the experimental results show that both Support Vector Machines (SVM) and Decision Tree (DT) were able to achieve an average accuracy of 100:0%, representing an improvement in the field.

In this study, we explore some possibilities of using aerial images captured by Unmanned Aerial Vehicles (UAV) and discuss the benefits of using them in the context of intelligent agriculture. A novel method that supports the detection and segmentation of tree crowns, the delineation of shadows, and which shows the direction of sunlight is presented. It uses simple observation strategies and commonly used digital image processing techniques such as visual color enhancement and perception, morphological operations, and segmentation based on a region growing method. The proposal is evaluated using a dataset with different types of crop areas and pasture lands. The results indicate that the proposal can effectively deal with the detection and segmentation of elements of interest in the scene, as well as the indication of the right side of the light source.

Computer vision is an area have proven to play an essential role in urban and rural applications like medical, agriculture, and remote sensing. The use of image processing methods for simulating the visual capability of robots plays a crucial role in the consolidation of smart farming. The understanding of the complexity of outdoor environments, where the robot performs its task, is an essential issue for the development of efficient processes of autonomous mobility, especially in areas with uneven illumination, unpredictable weather conditions, and different color shades. In this study, we present a new method to detect and segment tree trunks from unstructured environments where natural properties such as lighting and terrain shape form a variety of non-controlled conditions. We prepared a dataset with stereo image pairs and ground truth maps to calculate disparities and to evaluate the proposed method in the application of smart farming. The results show that the presented approach can segment trees with high precision, which is an important step in calculating the disparity of external components by systems that use the stereoscopic view.

As a digital image provides such information about a scene, a disparity map can be yielded by means of stereo images. This topic was exhaustively surveyed but it remains one of the most important branches in both computer vision and machine vision. Most algorithms are organized in a pipeline that starts with a matching cost step and ends with a disparity refinement. This paper provides a simple but an effective method to adjust a disparity map in a more appropriate configuration, i.e. it presents a disparity refinement technique. It is based on an assumption that most disparities in a region point to a correct disparity value for this area. To develop the methodology, we use image segmentation and support weighted windows. By performing an evaluation, it shows that this method can increase the robustness of a raw disparity map even with a lot of noisy parts.