The timely diagnosis of brain tumors is currently a complicated task. The objective was to build an image classification model to detect the existence or not of brain tumors by adding a classification header to a ResNet-50 architecture. The CRISP-DM methodology was used for data mining. A dataset of 3847 brain MRI images was used, 2770 images for training, 500 for validation, and 577 for testing. The images were resized to a 256 × 256 scale and then a data generator is created that is responsible for dividing pixels by 255. The training was performed and then the evaluation process was carried out, obtaining an accuracy percentage of 92% and a precision of 94% in the evaluation process. It is concluded that the proposed CNN model composed of a head with a ResNet50 architecture and a seven-layer convolutional network achieves adequate accuracy, becoming an efficient and complementary proposal to other models developed in previous works.

In the current global context, there has been a significant increase in respiratory system diseases, particularly pneumonia. This disease has a higher incidence of mortality in children under five years old and adults over 60 years old because it leads to complications if not treated in time. This research leverages convolutional neural networks (CNNs) to classify images, specifically to detect the presence of pneumonia. The data processing methodology utilized in this study is CRISP-DM. The dataset consists of 5,856 images of anteroposterior chest X-rays downloaded from the open repository “Kaggle,” divided into 5,216 images for training, 16 for validation, and 624 for testing. Preprocessing involved image augmentation through modifications to the original images, scaling, and batch division in tensor format. A comparative analysis was conducted among the transfer models: DenseNet, VGG19, and ResNet50 version 2. Each transfer model was the header of a CNN with four subsequent layers. The models underwent training, validation, and testing phases. The test’s results showed that DenseNet achieved an accuracy of 0.87, VGG19 achieved 0.86, and ResNet50 achieved 0.91. These results affirm the effectiveness of ResNet50 in image classification, considering that the model’s output is binary, where 0 represents that the patient does not have pneumonia and 1 indicates that the patient has pneumonia.

El dengue es una enfermedad arboviral que anualmente reporta un gran número de infectados en la costa norte y la selva peruana. Según las estadísticas, está aumentando cada año. Este artículo tiene como objetivo desarrollar una revisión sistemática de la literatura científica sobre las variables de estudio y los métodos de aprendizaje automático utilizados actualmente para detectar la infección por dengue. La metodología utilizada fue PRISMA, mapeando inicialmente la literatura de 274 artículos científicos, quedando seleccionados 33 artículos para la revisión sistemática. Los resultados obtenidos son que los algoritmos de aprendizaje automático más utilizados son las redes neuronales (NN) y support vector machine (SVM). Asimismo, se ha encontrado que los científicos tienden a realizar investigaciones con variables climáticas o demográficas para obtener mejores resultados. Se concluye que los métodos de aprendizaje automático que más se han utilizado son las redes neuronales de diferentes tipos: convolucional, recurrente, profunda y multicapa, y para la predicción de brotes de dengue predominaron los métodos de series de tiempo con LSTM y ARIMA, también se estableció que la tendencia es hacia la inclusión de variables climáticas y demográficas en los modelos de predicción.

The limited availability of standardized datasets has hindered the implementation of artificial intelligence (AI) models in serous fluid cytology, particularly in pleural fluid analysis. In this paper, we present the construction of a dataset of pleural fluid cytology images. The objective is to generate a dataset of pleural fluid cytologic images validated by two pathologists and classified into five categories for cell diagnosis, which will be used to train AI models. As a methodology, the images represent pleural fluid cytology samples that have been prepared through medical procedures and transferred to slides, providing valuable information when evaluated under the microscope by medical specialists through cytological examination. We documented the entire process for building the pleural fluid cytological image dataset, from image capture, labeling, preprocessing, standardization, and uploading to public platforms. As a result, we obtained a pleural fluid cytology dataset based on the International System (TIS) criteria for reporting serous fluids, classifying samples into AUS, MAL, ND, NFM, and SFM. This dataset is intended to support medical research, deep learning applications in medical image analysis, and improved diagnostic methodologies.

University-level learning requires autonomous student development, a need that traditional teaching methods fail to adequately address. This systematic review aims to determine whether there is an influence between the application of the Flipped Classroom (FC) model and the development of autonomous learning in university students through a comprehensive analysis of pedagogical strategies, technological resources, learning outcomes, and cross-curricular skill development across diverse academic disciplines. Using PRISMA methodology, a comprehensive literature search was conducted in Scopus database (2020-2024). From 198 initial publications, 15 studies met the inclusion criteria for systematic analysis. Results indicate that FC implementations predominantly utilize integrated technological ecosystems, including Learning Management Systems (Moodle), multimedia platforms (YouTube), social networking applications (WeChat), and interactive assessment tools. China and Spain emerge as research leaders with six publications each, demonstrating distinct approaches: Spanish studies emphasize pedagogical evaluation across traditional disciplines, while Chinese research integrates advanced technologies, including big data analytics, neural networks, and mobile learning platforms. Consistent positive outcomes include enhanced student autonomy, improved academic performance, increased classroom engagement, and development of cross-curricular competencies, including critical thinking, problem-solving, and metacognitive skills. In conclusion, the Flipped Classroom model creates an effective student-centered learning environment that significantly promotes autonomous learning development when combined with appropriate technological resources and complementary pedagogical approaches, establishing FC as a transformative educational strategy for contemporary higher education.