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.

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.

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.

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.

Identifying the phase or stage of children's spelling development is a regular activity in literacy classes. Usually, teachers assume some developmental theory and perform paper-and-pencil based tests. Existing digital tools often do not consider any of these known theories, nor do they capture the child's handwriting. Therefore, some teachers prefer to continue applying the tests manually. Accordingly, this study's research question is how to promote the performance of spelling tests on mobile devices, simulating the interaction between manual tests and the theoretical models already used by teachers. Through the Design Science Research Methodology (DSRM), we propose a method to apply child spelling tests in an automated way. In this work, we present the results of the child's interface usability evaluation in the developed computer artifact, using guidelines of Touchscreen Interaction Design Recommendations for Children (TIDRC). The results indicate adequacy to the recommendations of 88% of items in visual and audio features (cognitive dimension), 75% in the physical dimension, and 47% in socio-emotional dimension. These results are promising and relevant compared to previous studies that evaluated apps using the TIDRC framework.

Medical X-ray images are an important and valuable source of studies and diagnoses for diseases with low cost besides its high availability. However, radiological images are subject to degradations related to low contrast and presence of noise. Based on this finding, this article presents a simple but efficient enhancement method for these images with the objective of contrast gain and noise removal. The proposed method (MP) consists of a sequence of interactive steps. Start from the step of double precision conversion and end with removing impulsive noises. An evaluation with the PSNR, Entropy, AMBE, and IQR indicators was performed, besides gain check on the thresholding process and the histogram characterization. The evaluation was conducted on three different datasets in a total of 1409 images chest X-rays. The results compared to others known in the literature proved to be promising and put it as an interesting alternative in the process of enhancement medical X-ray images.