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All Journal Journal of Information Systems and Informatics
Ardian Mohib
Airlangga University
Computer Science & IT

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Lung X-ray Image Classification for Distinguishing Tuberculosis and Pneumonia Using Pretrained CNN Feature Extractors and Supervised Classifiers Ardian Mohib; Imam Yuadi; Ira Puspitasari; Yusi Dyah Patriani
Journal of Information System and Informatics Vol 8 No 3 (2026): June
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i3.1595

Abstract

Tuberculosis (TB) and pneumonia (PNA) are infectious lung diseases with overlapping chest X-ray (CXR) manifestations, making automated differential classification clinically important and methodologically challenging. This study proposes a supervised CXR classification workflow to distinguish TB from PNA using pretrained convolutional neural network (CNN) feature extractors and supervised classifiers. A publicly available de-identified dataset comprising 390 TB and 390 PNA images was used. Images were screened to exclude duplicates, corrupted files, non-CXR images, unclear labels, and identifiable cases. Preprocessing included format standardization, resizing according to CNN input requirements, and normalization. To reduce augmentation-based leakage risk, no heavy pre-validation augmentation was applied. Image embeddings were extracted using VGG-16, Inception V3, and VGG-19, then classified using Logistic Regression, Support Vector Machine, and Neural Network models. Performance was evaluated using stratified 5-fold cross-validation with AUC, accuracy, F1-score, precision, recall, MCC, and confusion matrix analysis. The Inception V3–Logistic Regression combination achieved the best performance, with AUC of 0.999, accuracy of 0.992, F1-score of 0.992, and MCC of 0.985.
Co-Authors Imam Yuadi Ira Puspitasari Yusi Dyah Patriani