Deep Learning Algorithms in Chest Images for Pneumonia Detection

• Autores: Porras, Fernando Tello, Rodriguez, Ciro, Rodriguez, Diego, Lezama, Pedro, Inquilla, Ricardo, Pomachagua, Yuri
• Formato: artículo
• Fecha de Publicación: 2022
• Institución: Universidad Peruana de Ciencias Aplicadas
• Repositorio: UPC-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorioacademico.upc.edu.pe:10757/669236
• Enlace del recurso: http://hdl.handle.net/10757/669236
• Nivel de acceso: acceso embargado
• Materia: chest images; CNN; Deep Learning architectures; Pneumonia

neumonia has become the respiratory disease that continuously causes deaths in the world; as a response to this serious problem, a literature review is performed to identify Deep Learning classification models for pneumonia detection with an accuracy higher than 95%. For the identification of the models, different architectures such as InceptionV3, MobileNet, MobileNetV2 Xception, VGG16, VGG19, DenseNet201, NasnetMobile, CNN, and LSTM were evaluated. Although they all show very acceptable accuracy indicators, which justifies their evaluation for model identification, the datasets were evaluated with chest X-ray images in different categories. As a result, it was determined that ResNet152V2 achieved an accuracy of 99.22%, which is considered one of the best models for pneumonia detection.