Abstract The motivation of this work is to investigate two technological AI paths, evaluate the performance, and discuss the results. Using a covid-19 chest X-ray images databank, we address the two distinct experiments to this problem: (1) an investigation of feature extraction and classification using machine learning algorithms and (2) an approach based on transfer learning used in state-of-the-art applications. For the implementation of our proposal (1), an integrated framework consisting of 25 algorithms with different characteristics was developed to extract features from chest X-ray images. Following this path, we seek to focus on the spatial spectral signatures of shape, texture, local and global statistical quantities. The extraction of features based on information in Fourier and wavelet space-frequency domain was also implemented as part of the framework. On the other hand, several transfer learning CNN’s were also used to evaluate performance and to compere to the first technological path results. Furthermore, the performance of other results reported by various other works are provided. The comparative performance evaluation demonstrated that the two concepts for a computational intelligence tool can produce very good results even working in high-dimensional vector spaces.

Abstract Gliomas comprise the vast majority of all malignant brain tumors. Low-grade glioma patients with combined whole-arm losses of 1p and 19q chromosomes were shown to have significantly better overall survival rates compared to non-deleted patients. This work evaluates several approaches for assessment of 1p/19q status from T2-weighted magnetic resonance images, using radiomics and machine learning. Experiments were performed using images from a public database (102 codeleted, 57 non-deleted). We experimented with sets of 68 and 100 radiomic features, and with several classifiers, including support vector machine, k-nearest neighbors, stochastic gradient descent, logistic regression, decision tree, Gaussian naive Bayes, and linear discriminant analysis. We also experimented with several ensemble-based methods, including four boosting-based classifiers, random forest, extra-trees, and bagging. The performance of these methods was compared using various metrics. Our best results were achieved using a bagging ensemble estimator based on the decision tree classifier, using only texture-based radiomics features. Compared to other works that used the same database, this approach provided higher sensitivity. It also achieved higher sensitivity than that provided by neurosurgeons and neuroradiologists analyzing the same images. We also show that including radiomic features associated with first order statistics and shape does not improve the performance of the classifiers, and in many cases worsens it. The molecular assessment of brain tumors through biopsies is an invasive procedure, and is subject to sampling errors. Therefore, the techniques presented in this work have strong potential for aiding in better clinical, surgical, and therapeutic decision-making.

INTRODUCTION: In the study of human biomechanics, it is often desirable to evaluate fatigue in the muscles that are involved in performing a particular task. Identifying the phenomena responsible for this condition is a problem that in most cases is complex and requires appropriate research mechanisms. Isokinetic dynamometry (ID) and surface electromyography (SEMG) are two techniques widely used in studies on strength and muscle fatigue. Their effectiveness is conditioned upon a good understanding of their limitations and the adoption of procedures to fully exploit the potential of each one. The main goal of the present study is to verify whether the electromyographic parameters, especially the conduction velocity (CV), are sensitive to the fatigue instauration process within sets of maximal isokinetic contractions. CV is a basic physiological parameter directly related to muscle activity and still little explored in experiments combining ID and SEMG. METHODS: Instrumentation architecture that combines ID and SEMG was used to estimate electromyographic and biomechanical parameters in protocols of maximum intensity isokinetic knee extension exercises. This architecture allows for limiting the parameter estimates to a specific region of isokinetic exercise, called the isokinetic load range (ILR), where one can consider that the angular velocity is constant and the SEMG signals are cyclo-stationary. Electromyographic signals were acquired using an array of electrodes. CONCLUSION: The results suggest that CV and the other SEMG parameters, including amplitude and frequency descriptors, are sensitive to detect a fatigue process only in protocols that restrict the analysis to ILR and that also bring the subject to a state of fatigue quickly.

INTRODUÇÃO: A Doença de Chagas é uma endemia rural, prevalente em grande parte da América Central e América do Sul e, aproximadamente, metade dos pacientes contaminados com o parasita Trypanosoma cruzi não apresentam sinais clínicos, eletrocardiográficos e radiológicos de envolvimento cardíaco. Este trabalho, entretanto, propõe uma técnica de auxílio ao diagnóstico da Doença de Chagas baseada em sinais de eletrocardiografia, que extrai informações relevantes desses sinais. MÉTODOS: Duas abordagens são estudadas e implementadas. Ambas utilizam sinais de variabilidade da frequência cardíaca (VFC) e classificação por meio de rede neural, mais especificamente, o mapa auto-organizável de Kohonen. A VFC, que reflete a modulação neural autonômica simpática e parassimpática do coração, é avaliada com base em séries contínuas de intervalos RR do ECG convencional registradas durante 5 minutos. Na primeira abordagem, indicadores estatístico-temporais obtidos diretamente dos sinais de VFC são utilizados como entrada da rede neural para treinamento e teste do método de classificação. Na segunda proposta, são utilizados escalogramas wavelet com função de base DoG (derivative of Gaussian) para avaliação dos sinais de VFC. Indicadores obtidos dos escalogramas são utilizados como entrada da rede neural no treinamento e no teste do algoritmo. Os mapas topológicos de Kohonen são utilizados para comparar a capacidade dos indicadores calculados dos sinais de VFC em discriminar pacientes chagásicos cardiopatas, chagásicos indeterminados e indivíduos normais. Os indicadores temporais convencionais e os indicadores escalográficos são comparados. RESULTADOS: Os resultados mostram que os indicadores escalográficos têm poder discriminatório estatisticamente superior aos indicadores temporais convencionais. Em particular, a potência média da densidade de potência do escalograma na banda de altas frequências mostrou ser estatisticamente o indicador de maior poder discriminatório (p < 0,05 para os 3 casos). CONCLUSÃO: A metodologia proposta mostrou-se capaz de distinguir entre indivíduos normais, chagásicos cardiopatas e chagásicos indeterminados. Os índices escalográficos propostos mostraram maior capacidade classificatória que os índices temporais tradicionais.

INTRODUCTION: Chagas' disease is an endemic rural disease prevalent in much of Central America and South America, and approximately half of the patients infected with the parasite Trypanosoma cruzi show no clinical, electrocardiographic and radiological cardiac involvement. This paper, however, proposes a technique for the diagnosis of Chagas' disease based on ECG signals, which extracts relevant information from these signals. METHODS: Two approaches are studied and implemented. Both approaches use heart rate variability (HRV) signals, and classification by a neural network, more specifically, the Kohonen self-organizing map. The HRV, which reflects sympathetic and parasympathetic autonomic neural modulation of the heart, is evaluated based on continuous series of RR intervals, calculated from 5-minute records of conventional ECG. In the first approach, statistical/temporal indexes obtained directly from the HRV signals are used as neural network inputs for training and testing of the classification method. In the second approach, derivative of Gaussian (DoG) wavelet scalograms are used to evaluate the HRV signals. Scalographic indexes are used as neural network inputs for training and testing of the algorithm. Kohonen topological maps are used to compare the ability of these HRV indicators of discriminating between patients with Chagas heart disease, Chagas indeterminate heart disease, and normal subjects. Conventional temporal indicators and indicators obtained from DoG scalograms are compared. RESULTS: Results of the application of the proposed methods to HRV signal databases, and performance comparisons, are presented. The results show that scalographic indicators have superior discriminatory power than conventional time-domain indicators. Based on an analysis of statistical significance, we show that the average power of the high-frequency band of the scalogram power spectral density is the indicator with greatest discriminatory power (p < 0,05 for all 3 cases). CONCLUSION: The proposed method has the ability to discriminate between normal subjects, subjects with Chagas cardiomyopathy, and subjects with the indeterminate form of Chagas' disease. It was observed that scalographic neural networks present greater discrimination ability than temporal neural networks.