Application of MobileNetV2 and SVM Combination for Enhanced Accuracy in Pneumonia Classification
Abstract
Pneumonia is a very common respiratory infection in low- and middle-income countries and is still a leading cause of death, especially among children under five years old. Modern technologies, such as machine learning, offer significant potential in improving the automatic detection of pneumonia through chest X-ray (CXR) image analysis. This study aims to develop a more accurate pneumonia diagnosis system by evaluating various feature extraction methods. CXR datasets of pneumonia patients were resized to 180x180 pixels and balanced using the SMOTE-Tomek technique. Three main approaches were investigated: direct classification using Support Vector Machine (SVM) on the SMOTE-Tomek balanced dataset, feature extraction using Sobel edge detection followed by SVM classification, and feature extraction using MobileNet-V2 followed by SVM classification. The results showed that Scheme 1 achieved 97% accuracy, Scheme 2 decreased to 95%, and Scheme 3 achieved the highest accuracy at 98%. The lower accuracy in Scheme 2 is due to the limitations of Sobel edge detection, which reduces the key features in the CXR image. On the other hand, the improvement in Scheme 3 is due to the effective feature extraction capability of MobileNet-V2. In conclusion, the choice of feature extraction method plays an important role in determining the accuracy of an automated diagnostic system. This study builds on existing research and is expected to make a significant contribution to the development of more accurate and efficient automated diagnostic systems, which can ultimately help reduce pneumonia-related mortality.
References
[2] A. Serener and S. Serte, “Deep learning for mycoplasma pneumonia discrimination from pneumonias like COVID-19,” in 4th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2020 - Proceedings, Institute of Electrical and Electronics Engineers Inc., Oct. 2020. doi: 10.1109/ISMSIT50672.2020.9254561.
[3] T. S. Arulananth, S. W. Prakash, R. K. Ayyasamy, V. P. Kavitha, P. G. Kuppusamy, and P. Chinnasamy, “Classification of Paediatric Pneumonia Using Modified DenseNet-121 Deep-Learning Model,” IEEE Access, vol. 12, pp. 35716–35727, 2024, doi: 10.1109/ACCESS.2024.3371151.
[4] M. Nahiduzzaman et al., “A novel method for multivariant pneumonia classification based on hybrid CNN-PCA based feature extraction using extreme learning machine with CXR images,” IEEE Access, vol. 9, pp. 147512–147526, 2021, doi: 10.1109/ACCESS.2021.3123782.
[5] R. K. Sheu, M. S. Pardeshi, K. C. Pai, L. C. Chen, C. L. Wu, and W. C. Chen, “Interpretable Classification of Pneumonia Infection Using eXplainable AI (XAI-ICP),” IEEE Access, vol. 11, pp. 28896–28919, 2023, doi: 10.1109/ACCESS.2023.3255403.
[6] H. Malik et al., “A Novel Fusion Model of Hand-Crafted Features With Deep Convolutional Neural Networks for Classification of Several Chest Diseases Using X-Ray Images,” IEEE Access, vol. 11, pp. 39243–39268, 2023, doi: 10.1109/ACCESS.2023.3267492.
[7] C. Wijaya, H. Irsyad, and W. Widhiarso, “Klasifikasi Pneumonia Menggunakan Metode K-Nearest Neighbor Dengan Ekstraksi GLCM,” 2020.
[8] A. Hussain, S. U. Amin, H. Lee, A. Khan, N. F. Khan, and S. Seo, “An Automated Chest X-Ray Image Analysis for Covid-19 and Pneumonia Diagnosis Using Deep Ensemble Strategy,” IEEE Access, vol. 11, pp. 97207–97220, 2023, doi: 10.1109/ACCESS.2023.3312533.
[9] E. F. Ohata et al., “Automatic detection of COVID-19 infection using chest X-ray images through transfer learning,” IEEE/CAA J. Autom. Sin., vol. 8, no. 1, pp. 239–248, 2021, doi: 10.1109/JAS.2020.1003393.
[10] S. Xue and C. Abhayaratne, “Region-of-Interest Aware 3D ResNet for Classification of COVID-19 Chest Computerised Tomography Scans,” IEEE Access, vol. 11, no. January, pp. 28856–28872, 2023, doi: 10.1109/ACCESS.2023.3260632.
[11] M. M. S. Fareed et al., “ADD-Net: An Effective Deep Learning Model for Early Detection of Alzheimer Disease in MRI Scans,” IEEE Access, vol. 10, pp. 96930–96951, 2022, doi: 10.1109/ACCESS.2022.3204395.
[12] Muljono, S. A. Wulandari, H. Al Azies, M. Naufal, W. A. Prasetyanto, and F. A. Zahra, “Breaking Boundaries in Diagnosis: Non-Invasive Anemia Detection Empowered by AI,” IEEE Access, vol. 12, no. November 2023, pp. 9292–9307, 2024, doi: 10.1109/ACCESS.2024.3353788.
[13] Z. Wang, C. Wu, K. Zheng, X. Niu, and X. Wang, “SMOTETomek-Based Resampling for Personality Recognition,” IEEE Access, vol. 7, pp. 129678–129689, 2019, doi: 10.1109/ACCESS.2019.2940061.
[14] K. M. Kahloot and P. Ekler, “Algorithmic Splitting: A Method for Dataset Preparation,” IEEE Access, vol. 9, pp. 125229–125237, 2021, doi: 10.1109/ACCESS.2021.3110745.
[15] A. H. Abdel-Gawad, L. A. Said, and A. G. Radwan, “Optimized Edge Detection Technique for Brain Tumor Detection in MR Images,” IEEE Access, vol. 8, pp. 136243–136259, 2020, doi: 10.1109/ACCESS.2020.3009898.
[16] A. Ainun, D. Halim, and S. Anraeni, “Analisis Klasifikasi Dataset Citra Penyakit Pneumonia Menggunakan Metode K-Nearest Neighbor (KNN),” Indones. J. Data Sci., vol. 2, no. 1, pp. 1–12, 2021.
[17] S. Ki Hong and Y. Lee, “Optimizing Detection: Compact MobileNet Models for Precise Hall Sensor Fault Identification in BLDC Motor Drives,” IEEE Access, vol. 12, pp. 77475–77485, 2024, doi: 10.1109/ACCESS.2024.3407766.
[18] D. Hartanti and A. I. Pradana, “Komparasi Algoritma Machine Learning dalam Identifikasi Kualitas Air,” SMARTICS J., vol. 9, no. 1, pp. 1–6, 2023, [Online]. Available: https://doi.org/10.21067/smartics.v9i1.8113
[19] U. Demirel, H. Cam, and R. Unlu, “Predicting stock prices using machine learning methods and deep learning algorithms: The sample of the istanbul stock exchange,” Gazi Univ. J. Sci., vol. 34, no. 1, pp. 63–82, 2021, doi: 10.35378/gujs.679103.
[20] H. Hananti and K. Sari, “Perbandingan Metode Support Vector Machine (SVM) dan Artificial Neural Network (ANN) pada Klasifikasi Gizi Balita,” Semin. Nas. Off. Stat., vol. 2021, no. 1, pp. 1036–1043, 2021, doi: 10.34123/semnasoffstat.v2021i1.1014.
[21] D. Diana Dewi, N. Qisthi, S. S. S. Lestari, and Z. H. S. Putri, “Perbandingan Metode Neural Network Dan Support Vector Machine Dalam Klasifikasi Diagnosa Penyakit Diabetes,” Cerdika J. Ilm. Indones., vol. 3, no. 09, pp. 828–839, 2023, doi: 10.59141/cerdika.v3i09.662.
[22] R. Zhang, Q. Xiao, Y. Du, and X. Zuo, “DSPI Filtering Evaluation Method Based on Sobel Operator and Image Entropy,” IEEE Photonics J., vol. 13, no. 6, Dec. 2021, doi: 10.1109/JPHOT.2021.3118924.
[23] A. Nur, A. Thohari, A. Karima, K. Santoso, and R. Rahmawati, “Crack Detection in Building Through Deep Learning Feature Extraction and Machine Learning Approach,” vol. 8, no. 1, pp. 1–6, 2024.99
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