Exploring Ensemble  Architectures on Lung X-Ray Multi-Class Image for Classification Using Convolutional Neural Network and Random Forest

Devin Garmenta  Nuriansyah; Putu Desiana Wulaning  Ayu; Dandy Pramana  Hostiadi

doi:10.52436/1.jutif.2026.7.2.5016

Authors

Devin Garmenta Nuriansyah Magister Program, Department of Magister Information Systems, Institut Teknologi dan Bisnis STIKOM Bali, Jln. Raya Puputan No. 86, Denpasar 80234, Indonesia
Putu Desiana Wulaning Ayu Department of Magister Information Systems, Institut Teknologi dan Bisnis STIKOM Bali, Jln. Raya Puputan No. 86, Denpasar 80234, Indonesia
Dandy Pramana Hostiadi Department of Magister Information Systems, Institut Teknologi dan Bisnis STIKOM Bali, Jln. Raya Puputan No. 86, Denpasar 80234, Indonesia

DOI:

https://doi.org/10.52436/1.jutif.2026.7.2.5016

Keywords:

Augmentation, Chest X-Ray, Convolutional Neural Network, Feature Extraction, MobileNet, Random Forest

Abstract

The lungs are vital organs that play an important role in the respiratory and circulatory systems. Early detection of lung diseases through medical images, especially Chest X-Ray (CXR), is still a challenge due to the limited amount of data and complexity in image interpretation. This research aims to develop an effective image classification approach for lung disease detection by comparing two main methods: direct training using Convolutional Neural Network (CNN) and a hybrid method involving feature extraction from CNN model, feature selection using Chi-Square method, and classification using Random Forest algorithm.

To overcome data imbalance and increase variation, data augmentation techniques such as rotation, vertical and horizontal flipping, and zooming are used. Four popular CNN architectures are used in training, namely VGG16, ResNet-50, InceptionV3, and MobileNet. After training, features are extracted and stored in .csv format. Next, feature selection using the Chi-Square method and classification with Random Forest are performed.

The experimental results show that direct CNN training achieves high accuracy, with MobileNet reaching the highest performance at 98.83%. However, this approach requires significant computational resources and longer training time. In contrast, the hybrid method offers competitive accuracy with lower computational demands. The findings highlight the potential of combining deep learning and traditional machine learning to create efficient, accurate, and resource-friendly medical image classification systems. This research has significant implications for supporting early diagnosis of lung diseases, reducing diagnostic workload for medical professionals, and enabling the development of deployable AI-assisted healthcare solutions in resource-limited settings.

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