Classification Model Using Transfer Learning for the Detection of Pneumonia in Chest X-Ray Images

Authors

DOI:

https://doi.org/10.3991/ijoe.v20i05.45277

Keywords:

Classification models, Transfer learning, Convolutional neural networks, Pneumonia, Data augmentation, Image Data Generator

Abstract


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.

Author Biography

Gisella Luisa Elena Maquen-Niño, Universidad Nacional Pedro Ruiz Gallo

Professor at the Universidad Nacional Pedro Ruiz Gallo, Department of Electronics and Computer Science, Lambayeque - Peru. Hired professor at the Universidad Mayor de San Marcos, Lima - Peru. PhD in Computer Science and Engineering at the Universidad Señor de Sipan - Peru. Diploma in Machine Learning and Deep Learning for Engineering at Universidad Católica San Pablo. With more than 13 years of undergraduate and graduate teaching experience.

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Published

2024-03-15

How to Cite

Maquen-Niño, G. L. E., Nuñez-Fernandez, J. G., Taquila-Calderon, F. Y., Adrianzén-Olano, I., De-La-Cruz-VdV, P., & Carrión-Barco, G. (2024). Classification Model Using Transfer Learning for the Detection of Pneumonia in Chest X-Ray Images. International Journal of Online and Biomedical Engineering (iJOE), 20(05), pp. 150–161. https://doi.org/10.3991/ijoe.v20i05.45277

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Section

Papers