Pneumonia Diagnosis Through Deep Learning: ResNet50v2 Model Implementation

Yufis Azhar; Wahyu Priyo Wicaksono; Zamah Sari; Wahyu Priyo Wicaksono

doi:10.23887/janapati.v13i2.72068

Authors

Yufis Azhar
Wahyu Priyo Wicaksono Universitas Muhammadiyah Malang
Zamah Sari
Wahyu Priyo Wicaksono

DOI:

https://doi.org/10.23887/janapati.v13i2.72068

Abstract

Pneumonia is a significant global health concern, particularly affecting young children and the elderly. It is a lung infection caused by bacteria, viruses, fungi, or parasites, leading to the alveoli filling with pus or fluid. This study addresses the challenge of accurately diagnosing pneumonia using chest X-ray images, a process traditionally dependent on the expertise of radiologists. The reliance on radiologists results in lengthy diagnosis times and high costs, particularly in regions with a shortage of medical professionals. This research presents a deep-learning approach to automate the classification of pneumonia using the ResNet50v2 model, which has been pre-trained on the ImageNet dataset. The dataset used in this study, obtained from the Guangzhou Women and Children’s Medical Center, comprises 5,856 images, with 1,583 normal and 4,273 pneumonia cases. The images were preprocessed and augmented to enhance the model's robustness. The proposed model achieved an accuracy of 94%, demonstrating its potential in clinical settings to assist in the rapid and reliable diagnosis of pneumonia. This study contributes to the growing body of research in medical image analysis by employing a pre-trained ResNet50v2 model. It highlights the importance of leveraging advanced machine-learning techniques to improve diagnostic accuracy and efficiency.

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