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All Journal TIN: TERAPAN INFORMATIKA NUSANTARA
Benny Sukma Negara
Universitas Islam Negeri Sultan Syarif Kasim, Riau
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Penerapan Semi-Supervised Deep Learning dengan Remixmatch untuk Klasifikasi Penyakit Paru-Paru Menggunakan Citra Chest X-Ray Fajri Fajri; Benny Sukma Negara; Muhammad Irsyad; Febi Yanto; Iis Afrianty
TIN: Terapan Informatika Nusantara Vol 7 No 2 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i2.10382

Abstract

Lung diseases such as pneumonia and COVID-19 viral infection remain significant health problems that require fast and accurate diagnostic processes. The utilization of deep learning-based Computer-Aided Diagnosis (CAD) on Chest X-Ray (CXR) images has demonstrated promising capabilities in assisting disease classification. However, the implementation of deep learning models in the medical field still faces a major challenge, namely the limited availability of labeled data due to the time-consuming annotation process and the involvement of medical experts. This study applies a semi-supervised learning approach using the ReMixMatch algorithm with DenseNet169 architecture as a feature extraction backbone to reduce the dependency on large amounts of labeled data. Experiments were conducted using the public dataset Covid19-Pneumonia-Normal Chest X-Ray Images available on Mendeley Data. The ReMixMatch method utilizes both labeled and unlabeled data through pseudo-labeling, distribution alignment, MixUp augmentation, and consistency regularization mechanisms during the model training process. The evaluation was performed using several labeled data scenarios, namely 10, 20, 30, and 40 labels per class. The experimental results show that the combination of ReMixMatch and DenseNet169 achieved high classification performance with an accuracy of 96.43% on the validation data. The model evaluation obtained a precision value of 96.47%, a recall value of 96.43%, and an F1-score of 96.42%. These results indicate that the semi-supervised learning approach is able to effectively utilize information from unlabeled data, thereby maintaining high Chest X-Ray image classification performance under limited annotation conditions. This study offers an alternative approach to developing a chest X-ray image classification system through the application of the ReMixMatch algorithm combined with the DenseNet169 architecture, enabling the model to achieve good classification performance even with a limited amount of labeled data.
DenseNet121 sebagai Feature Extractor pada Denoising Autoencoder untuk Deteksi Anomali Unsupervised Citra X-Ray Dada Raihan Muhammar Zikra; Febi Yanto; Benny Sukma Negara; Surya Agustian; Reski Mai Candra
TIN: Terapan Informatika Nusantara Vol 7 No 3 (2026): August 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i3.10860

Abstract

Anomaly detection in chest X-ray images remains a challenge in medical imaging, as supervised learning approaches require large amounts of labeled data that are difficult and costly to annotate. This study proposes an unsupervised learning anomaly detection system that integrates a pretrained DenseNet121 as a feature extractor with a Denoising Autoencoder (DAE), so that training requires only normal images without anomaly annotations. The model was trained on normal images and tested on COVID-19 and pneumonia images to evaluate its anomaly detection capability based on reconstruction error relative to an optimal threshold. Evaluation was conducted on the Covid19-Pneumonia-Normal Chest X-Ray Images dataset comprising 5,228 images, comparing the performance of DenseNet121 and ResNet50 as feature extractors across three latent dimension configurations. The DenseNet121 configuration with a latent dimension of 128 achieved the highest overall performance on most metrics, namely 91% accuracy, 90.75% sensitivity, 72% Macro F1-Score, and a validation loss (MSE) of 0.0952 on 3,608 test images, although its AUC (0.9526) and specificity (87.36%) were not consistently the highest among all tested configurations. These results demonstrate that using DenseNet121 as a feature extractor improves the DAE's ability to distinguish normal from anomalous lung images, suggesting its potential as an efficient preliminary screening approach under conditions of limited labeled data.
Deteksi Anomali pada Citra X-ray Dada Menggunakan Variational Autoencoder dengan Skema Sequential Hyperparameter Optimization Diki Aulio Fransisko; Febi Yanto; Benny Sukma Negara; Siti Ramadhani; Reski Mai Candra
TIN: Terapan Informatika Nusantara Vol 7 No 3 (2026): August 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i3.10864

Abstract

The limited availability of labeled medical images remains a major challenge in developing reliable deep learning-based disease detection systems. Conventional classification approaches generally require a large amount of abnormal data, whereas medical image annotation is time-consuming, costly, and highly dependent on radiological expertise. This study proposes an unsupervised anomaly detection model for chest X-ray images using a Variational Autoencoder (VAE), in which only normal images are utilized during the training process. The experiments were conducted on the COVID-19-Pneumonia-Normal Chest X-ray Images dataset, consisting of 5,228 images categorized into normal, pneumonia, and COVID-19 classes. The proposed framework includes image preprocessing, baseline VAE construction, sequential hyperparameter optimization, Beta-VAE implementation, and model evaluation using Accuracy, Precision, Recall, F1-Score, and the Area Under the Receiver Operating Characteristic Curve (AUROC). Experimental results demonstrate that the optimized model outperformed the baseline model, achieving an Accuracy of 97.50%, Precision of 96.32%, Recall of 100%, F1-Score of 98.12%, and an AUROC of 0.9999. These findings indicate that hyperparameter optimization and appropriate β coefficient selection improve latent representation learning, leading to more effective discrimination between normal and abnormal chest X-ray images. Therefore, the proposed approach has the potential to serve as an artificial intelligence-based early screening tool for chest radiograph analysis, particularly in scenarios where labeled medical data are limited.
Co-Authors Diki Aulio Fransisko Fajri Fajri Febi Yanto Iis Afrianty Muhammad Irsyad Raihan Muhammar Zikra Reski Mai Candra Siti Ramadhani Surya Agustian