Transfer Learning dengan CLAHE dan Sharpening filter untuk Deteksi Pneumonia pada Citra X-Ray
DOI:
https://doi.org/10.37859/jf.v16i1.11374
Abstract
Pneumonia is a respiratory infection that remains a leading cause of death, especially in children, requiring an automatic detection system based on chest X-ray images. The main challenge in automatic classification is low image quality, such as suboptimal contrast and unclear lung details, which can affect the feature extraction process by deep learning models. To address these issues, this study applies Contrast Limited Adaptive Histogram Equalization (CLAHE) to enhance image contrast and a sharpening filter to clarify lung edge details. The study aims to analyze the effect of preprocessing on classification performance using EfficientNet-B0 based on Transfer Learning with a full fine-tuning strategy. The dataset used is Chest X-Ray Pneumonia from Kaggle with 5,856 images consisting of Normal and Pneumonia classes. Experiments compare the Baseline model, CLAHE, and a combination of CLAHE and sharpening in three data sharing scenarios. Evaluation is carried out using accuracy, precision, recall, and image quality metrics PSNR, SSIM, and CII. The results of the study showed that the combination of CLAHE and sharpening in the 80:10:10 scenario produced the best performance with an accuracy of 97.61%, precision of 0.97, recall of 0.99, and an increase in image quality based on a CII value of 1.157.
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P. W. Dwiyanti and D. Hisni, “Analisis Asuhan Keperawatan melalui Intervensi Kolaborasi Pemberian Nebulizer dan Batuk Efektif pada Pasien Ny.P dan Tn.W dengan Diagnosa Medis Pneumonia di Wilayah RS DKI Jakarta,” Jurnal Kreativitas Pengabdian Kepada Masyarakat (PKM), vol. 7, no. 4, pp. 1654–1665, Apr. 2024, doi: 10.33024/jkpm.v7i4.13837.
A. Safitri, N. P. Alya, S. Salsabila N, H. Fauzi, and J. Ariyanto, “Kematian Anak akibat Pneumonia : Tinjauan Literatur tentang Peran Deteksi Dini dan Intervensi Medis,” GALENICAL: Jurnal Kedokteran dan Kesehatan Mahasiswa Malikussaleh, vol. 4, no. 4, pp. 55–67, 2025.
Kemenkes RI, Profil Kesehatan Indonesia. Jakarta: Kementerian Kesehatan Republik Indonesia, 2023.
I. Kurniawati, R. Akbar, Y. Ardina Kusuma, and I. Fahma Kusumawati, “Identifikasi Pneumonia pada Balita melalui Citra X-ray Menggunakan Metode Convolutional Neural Network (CNN),” Journal of Manufacturing in Industrial Engineering & Technology, vol. 3, no. 1, pp. 10–23, Jun. 2024, doi: 10.30651/mine-tech.v3i1.23391.
S. A. Adzkia and T. Arifin, “Perbandingan Arsitektur CNN Berbasis Transfer Learning untuk Klasifikasi pada BreastMNIST,” Jurnal Nasional Teknologi dan Sistem Informasi, vol. 11, no. 2, pp. 192–200, Sep. 2025, doi: 10.25077/teknosi.v11i2.2025.192-200.
R. K. Pasha and K. Budiman, “Optimasi Model CNN Berbasis Transfer Learning Untuk Klasifikasi Pneumonia pada Citra X-Ray Dada,” SMATIKA JURNAL, vol. 15, no. 01, pp. 167–178, Jun. 2025, doi: 10.32664/smatika.v15i01.1985.
N. Sebastian and B. Ankayarkanni, “Enhanced ResNet-50 with Multi-Feature Fusion for Robust Detection of Pneumonia in Chest X-Ray Images,” Diagnostics, vol. 15, no. 16, pp. 1–23, Aug. 2025, doi: 10.3390/diagnostics15162041.
L. Li, Z. Tan, and X. Han, “An Improved EfficientNet Model and its Applications in Pneumonia Image Classification,” Journal of Engineering Science and Technology Review, vol. 15, no. 6, pp. 49–54, 2022, doi: 10.25103/jestr.156.07.
M. A. Anwar, Y. A. Gerhana, and U. Syaripudin, “Implementasi Model CNN ResNet50V2 untuk Klasifikasi Pneumonia pada Citra X-Ray,” SMATIKA JURNAL, vol. 15, no. 01, pp. 126–135, Jun. 2025, doi: 10.32664/smatika.v15i01.1538.
A. D. Azzumzumi, M. Hanafi, and W. M. P. Dhuhita, “Klasifikasi Penyakit Paru-Paru Berdasarkan Peningkatan Kualitas Kontras dan EfficientNet Menggunakan Gambar X-Ray,” Teknika, vol. 13, no. 2, pp. 293–300, Jul. 2024, doi: 10.34148/teknika.v13i2.881.
D. Rizki Chandranegara, V. Dwi Vitanti, W. Suharso, H. Wibowo, and S. Arifianto, “Analysis of Pneumonia on Chest X-Ray Images Using Convolutional Neural Network Model ResNet-RS,” International Journal of Informatics Visualization, vol. 8, no. 1, pp. 183–189, 2024, [Online]. Available: www.joiv.org/index.php/joiv
K. U. Ahamed et al., “A deep learning approach using effective preprocessing techniques to detect COVID-19 from chest CT-scan and X-ray images,” Comput. Biol. Med., vol. 139, Dec. 2021, doi: 10.1016/j.compbiomed.2021.105014.
M. A. Fadila, B. Irawan, A. Faqih, R. D. Dana, and A. R. Renaldi, “Analisis Korelasi Restorasi Visual Dan Performa Klasifikasi Metode Image Sharpening Pada Daun Tomat,” JOURNAL OF COMPUTER SCIENCE AND ARTIFICIAL INTELLIGENCE (JCSAI), vol. 01, no. 03, 2026, [Online]. Available: https://ikmi.ac.id/page/18/?lang=de
G. A. Sandag, T. J. Mulalinda, G. A. M. Susanto, and S. R. Pungus, “Advanced pneumonia classification using transfer learning on chest X-ray data with EfficientNet and ResNet,” TELKOMNIKA (Telecommunication Computing Electronics and Control), vol. 23, no. 5, p. 1304, Oct. 2025, doi: 10.12928/telkomnika.v23i5.26387.
Khairudin, Bobi Agustian, and B. Nursakinah, “Klasifikasi Citra Medis Penyakit Pneumonia dengan Metode Convotional Neural Network,” Bulletin of Computer Science Research, vol. 5, no. 4, pp. 763–769, Jun. 2025, doi: 10.47065/bulletincsr.v5i4.576.
M. Nur Halizah, S. Najla, S. Amala Putri, N. Febita, M. Luthfi Hidayat, and M. Surakarta Abstrak, “Perbandingan Kinerja Artificial Neural Network Random Forest dan LightGBM dalam Prediksi Penyakit Jantung Menggunakan Dataset Kaggle,” Jurnal Ilmiah Wahana Pendidikan, vol. 12, no. C, pp. 196–209, 2026.
A. Shavkatovich Buriboev, A. Abduvaitov, and H. S. Jeon, “Binary Classification of Pneumonia in Chest X-Ray Images Using Modified Contrast-Limited Adaptive Histogram Equalization Algorithm,” Sensors, vol. 25, no. 13, Jul. 2025, doi: 10.3390/s25133976.
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