Introducción a la Clasificación de Neuroseñales utilizando Técnicas Clásicas y Modernas de Machine Learning en Google Colaboratory [Not available in English]

Ferrin-Bolaños, Carlos; Valero-Echeverri, Laura; Correa, Humberto Loaiza; Mosquera-DeLaCruz, Jose; Velez-Angel, Paulo

doi:10.1109/stsiva53688.2021.9591670

Cited by 1 publication

(1 citation statement)

References 24 publications

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“…La matriz de confusión [40] según Fig. 3 es un mecanismo del cual se desprenden las métricas más intuitivas y sencillas que se utiliza para encontrar la precisión, exactitud, sensibilidad entre otras, es muy usada en problemas de clasificación donde la salida puede ser de dos o más tipos de clases [41], permite la visualización de errores y aciertos que tuvo el modelo en datos de validación o pruebas; contiene columnas y filas, donde las filas se refieren a las etiquetas reales del modelo y las columnas son las predicciones hechas por el modelo.…”

Section: Métricas De Evaluaciónunclassified

Detection of Covid-19 through the Analysis of Radiographic Images of the Chest Using Convolutional Neural Networks

Morales¹,

Rodríguez²,

Patiño-Pérez³

et al. 2022

Proceedings of the 2nd LACCEI International Multiconference on Entrepreneurship, Innovation and Regional Development (LEIRD 202

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There are different ways to detect Covid-19, which have emerged so far giving an effective response in detecting the disease, the PCR test is a reliable diagnostic method, which requires a well-equipped laboratory to obtain results, which can take hours or days. Another detection technique for this disease is by analyzing the chest image; This technique is used as a diagnostic tool in emergency areas in health centers, because it can reveal characteristics related to lung involvement. For this reason, it is important to develop an automatic detection system, as an alternative diagnosis option for Covid-19. Deep Learning techniques can help detect the SARS-CoV-2 virus by analyzing chest radiographic images.Thanks to the high availability of the datasets available, and using convolutional neural networks, the analysis is carried out by classifying images. In this research, two CNN models were created whose outputs are normal or covid19, the same ones that were trained with two datasets from public research repositories. The performance of the models trained in Pytorch were compared with the models trained in Keras under similar conditions of parameters and hyperparameters, obtaining a higher performance with Pytorch however since the two types of models have learned adequately with an accuracy that is above the 90% recommended the use of both models.

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Section: Métricas De Evaluaciónunclassified