Convolutional Neural Networks with Transfer Learning for Pneumonia Detection

• Autores: Iparraguirre-Villanueva, Orlando, Guevara-Ponce, Victor, Roque Paredes, Ofelia, Sierra-Liñan, Fernando, Zapata-Paulini, Joselyn, Cabanillas-Carbonell, Michael
• Formato: artículo
• Fecha de Publicación: 2022
• Institución: Universidad Autónoma del Perú
• Repositorio: AUTONOMA-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.autonoma.edu.pe:20.500.13067/2612
• Enlace del recurso: https://hdl.handle.net/20.500.13067/2612; https://doi.org/10.14569/IJACSA.2022.0130963
• Nivel de acceso: acceso abierto
• Materia: Neural networks; Transfer learning; Pneumonia; Detection; Convolutional; https://purl.org/pe-repo/ocde/ford#2.02.04

Pneumonia is a type of acute respiratory infection caused by microbes, and viruses that affect the lungs. Pneumonia is the leading cause of infant mortality in the world, accounting for 81% of deaths in children under five years of age. There are approximately 1.2 million cases of pneumonia in children under five years of age and 180 000 died in 2016. Early detection of pneumonia can help reduce mortality rates. Therefore, this paper presents four convolutional neural network (CNN) models to detect pneumonia from chest X-ray images. CNNs were trained to classify X-ray images into two types: normal and pneumonia, using several convolutional layers. The four models used in this work are pre-trained: VGG16, VGG19, ResNet50, and InceptionV3. The measures that were used for the evaluation of the results are Accuracy, recall, and F1-Score. The models were trained and validated with the dataset. The results showed that the Inceptionv3 model achieved the best performance with 72.9% accuracy, recall 93.7%, and F1-Score 82%. This indicates that CNN models are suitable for detecting pneumonia with high accuracy.