Comparative Analysis of SVM and CNN for Pneumonia Detection in Chest X-Ray
Abstract
Recognizing pneumonia sufferers can be done by analyzing chest X-ray images. Pneumonia sufferers experience pleural effusion, where fluid is between the lungs’ layers. It causes the lungs’ X-ray picture to be cloudy or hazy. It differs from the appearance of X-rays on normal lungs which are dark in color. These differences in X-Ray images can be classified automatically with the help of Artificial Intelligence This research used convolutional neural networks and support vector machine methods to recognize X-ray images of pneumonia. This research applied Principal Component Analysis and Wavelet Transformation support to both methods. This research aimed to evaluate the performance of each model combination. The PCA-SVM model gave the best performance, with an accuracy of 94.545% and an F1 score of 94.675%. The SVM model outperforms the CNN model in recognizing images; in this case, it could be due to the relatively small amount of training data.
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References
[1] S. Amanullah, “Typical Bacterial Pneumonia Imaging,” Medscape. Accessed: Feb. 26, 2024. [Online]. Available: https://emedicine.medscape.com/article/360090-overview
[2] R. Kundu, R. Das, Z. W. Geem, G. T. Han, and R. Sarkar, “Pneumonia detection in chest X-ray images using an ensemble of deep learning models,” PLoS One, vol. 16, no. 9, Sep. 2021, doi: 10.1371/JOURNAL.PONE.0256630.
[3] “Fluid Around the Lungs or Malignant Pleural Effusion,” Cancer.Net. Accessed: Feb. 26, 2024. [Online]. Available: https://www.cancer.net/coping-with-cancer/physical-emotional-and-social-effects-cancer/managing-physical-side-effects/fluid-around-lungs-or-malignant-pleural-effusion
[4] M. A. Almaiah et al., “Performance Investigation of Principal Component Analysis for Intrusion Detection System Using Different Support Vector Machine Kernels,” Electronics. 2022, Vol. 11, Page 3571, vol. 11, no. 21, p. 3571, Nov. 2022, doi: 10.3390/ELECTRONICS11213571.
[5] W. Zhou, H. Wang, and Z. Wan, “Ore Image Classification Based on Improved CNN,” Computers and Electrical Engineering, vol. 99, p. 107819, Apr. 2022, doi: 10.1016/J.COMPELECENG.2022.107819.
[6] M. E. Paoletti, J. M. Haut, X. Tao, J. P. Miguel, and A. Plaza, “A New GPU Implementation of Support Vector Machines for Fast Hyperspectral Image Classification,” Remote Sensing, vol. 12, no. 8, p. 1257, Apr. 2020, doi: 10.3390/RS12081257.
[7] C. Tchito Tchapga et al., “Biomedical Image Classification in a Big Data Architecture Using Machine Learning Algorithms,” Journal of Healthcare Engineering, vol. 2021, 2021, doi: 10.1155/2021/9998819.
[8] D. A. Otchere, T. O. Arbi Ganat, R. Gholami, and S. Ridha, “Application of supervised machine learning paradigms in the prediction of petroleum reservoir properties: Comparative analysis of ANN and SVM models,” Journal of Petroleum Science and Engineering, vol. 200, p. 108182, May 2021, doi: 10.1016/J.PETROL.2020.108182.
[9] Q. Yang, H. Zhang, J. Xia, and X. Zhang, “Evaluation of magnetic resonance image segmentation in brain low-grade gliomas using support vector machine and convolutional neural network,” Quantitative Imaging in Medicine and Surgery, vol. 11, no. 1, p. 300, Jan. 2021, doi: 10.21037/QIMS-20-783.
[10] B. Gaye, D. Zhang, and A. Wulamu, “Improvement of Support Vector Machine Algorithm in Big Data Background,” Mathematical Problems in Engineering, vol. 2021, 2021, doi: 10.1155/2021/5594899.
[11] S. Mawarni, “Medical External Wound Image Classification Using Support Vector Machine Technique,” Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika, vol. 9, no. 2, pp. 98–103, 2023.
[12] J. B. A. Das, A. Das, A. Sarangi, D. Mishra, and M. N. Mohanty, “Cancerous image classification using support vector machine with optimized statistical features,” in AIP Conference Proceedings, vol. 2904, no. 1, p. 20010, Oct. 2023, doi: 10.1063/5.0171453.
[13] E. M. Senan and M. E. Jadhav, “Diagnosis of Dermoscopy Images for the Detection of Skin Lesions Using SVM and KNN,” in Proceedings of Third International Conference on Sustainable Computing, R. C. Poonia, V. Singh, D. Singh Jat, M. J. Diván, and M. S. Khan, Eds., Singapore: Springer Nature Singapore, 2022, pp. 125–134.
[14] A. Murugan, S. A. H. Nair, and K. P. S. Kumar, “Detection of Skin Cancer Using SVM, Random Forest and kNN Classifiers,” Journal of Medical Systems, vol. 43, no. 8, 2019, doi: 10.1007/s10916-019-1400-8.
[15] D. Chen et al., “Novel joint algorithm based on EEG in complex scenarios,” Computer Assisted Surgery, vol. 24, no. sup2, pp. 117–125, 2019, doi: 10.1080/24699322.2019.1649078.
[16] F. Liu and M. Brown, Breast cancer recognition by support vector machine combined with Daubechies wavelet transform and principal component analysis, vol. 30. Springer International Publishing, 2019. doi: 10.1007/978-3-030-00665-5_177.
[17] H. P. Raju Kunadharaju, S. N., and R. Mehra, “Detection of Brain Tumor Using Unsupervised Enhanced K-Means, PCA and Supervised SVM Machine Learning Algorithms,” International Research Journal on Advanced Science Hub, vol. 02, no. Special Issue ICSTM 12S, pp. 62–67, 2020, doi: 10.47392/irjash.2020.262.
[18] I. W. P. Pratama, M. W. A. Kesiman, and I. G. A. Gunadi, “Frequency Band and PCA Feature Comparison for EEG Signal Classification,” Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, vol. 12, no. 1, p. 1, 2021, doi: 10.24843/lkjiti.2021.v12.i01.p01.
[19] I. G. A. A. D. Indradewi, N. W. S. Saraswati, and N. W. Wardani, “COVID-19 Chest X-Ray Detection Performance Through Variations of Wavelets Basis Function,” MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 21, no. 1, pp. 31–42, Nov. 2021, doi: 10.30812/matrik.v21i1.1089.
[20] N. W. S. Saraswati, N. W. Wardani, and I. G. A. A. D. Indradewi, “Detection of Covid Chest X-Ray using Wavelet and Support Vector Machines,” International Journal of Engineering and Emerging Technology, vol. 5, no. 2, pp. 116–121, Dec. 2020, doi: 10.24843/IJEET.2020.V05.I02.P019.
[21] D. N. Avianty, P. I. G. P. S. Wijaya, and F. Bimantoro, “The Comparison of SVM and ANN Classifier for COVID-19 Prediction,” Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, vol. 13, no. 2, p. 128, 2022, doi: 10.24843/lkjiti.2022.v13.i02.p06.
[22] I. P. B. G. Prasetyo Raharja, I. M. Suwija Putra, and T. Le, “Kekarangan Balinese Carving Classification Using Gabor Convolutional Neural Network,” Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, vol. 13, no. 1, p. 1, 2022, doi: 10.24843/lkjiti.2022.v13.i01.p01.
[23] K. K. Gv and G. M. Reddy, “Automatic classification of whole slide pap smear images using CNN with PCA based feature interpretation,” IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, vol. 2019-June, pp. 1074–1079, 2019, doi: 10.1109/CVPRW.2019.00140.
[24] H. Basak, R. Kundu, S. Chakraborty, and N. Das, “Cervical Cytology Classification Using PCA and GWO Enhanced Deep Features Selection,” SN Computer Science, vol. 2, no. 5, 2021, doi: 10.1007/s42979-021-00741-2.
[25] M. Ahmadi, A. Sharifi, M. Jafarian Fard, and N. Soleimani, “Detection of brain lesion location in MRI images using convolutional neural network and robust PCA,” International Journal of Neuroscience, vol. 133, no. 1, pp. 55–66, 2023, doi: 10.1080/00207454.2021.1883602.
[26] Priyanka and D. Kumar, “Feature Extraction and Selection of kidney Ultrasound Images Using GLCM and PCA,” Procedia Computer Science, vol. 167, pp. 1722–1731, 2020, doi: https://doi.org/10.1016/j.procs.2020.03.382.
[27] R. Sharma and A. Singh, “An Integrated Approach towards Efficient Image Classification Using Deep CNN with Transfer Learning and PCA,” Advances in Technology Innovation, vol. 7, no. 2, pp. 105–117, 2022, doi: 10.46604/aiti.2022.8538.
[28] M. Wulandari, Basari, and D. Gunawan, “Evaluation of wavelet transform preprocessing with deep learning aimed at palm vein recognition application,” AIP Conference Proceedings, vol. 2193, no. 1, p. 50005, Dec. 2019, doi: 10.1063/1.5139378.
[29] H. Hasan, H. Z. M. Shafri, and M. Habshi, “A Comparison between Support Vector Machine (SVM) and Convolutional Neural Network (CNN) Models for Hyperspectral Image Classification,” IOP Conference Series: Earth and Environmental Science, vol. 357, no. 1, 2019, doi: 10.1088/1755-1315/357/1/012035.
[30] D. Anggriandi, E. Utami, and D. Ariatmanto, “Comparative Analysis of CNN and CNN-SVM Methods For Classification Types of Human Skin Disease,” Sinkron : Jurnal dan Penelitian Teknik Informatika, vol. 8, no. 4, pp. 2168–2178, 2023.
[31] A. Peryanto, A. Yudhana, and R. Umar, “Convolutional Neural Network and Support Vector Machine in Classification of Flower Images,” Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika, vol. 8, no. 1, pp. 1–7, Mar. 2022, doi: 10.23917/KHIF.V8I1.15531.
[32] N. Wayan et al., “Recognizing Pneumonia Infection in Chest X-Ray Using Deep Learning,” MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 23, no. 1, pp. 17–28, Oct. 2023, doi: 10.30812/MATRIK.V23I1.3197.
[33] J. P. Cohen, P. Morrison, L. Dao, K. Roth, T. Q. Duong, and M. Ghassemi, “COVID-19 Image Data Collection: Prospective Predictions Are the Future,” pp. 1–38, 2020.
[34] M. Utami et al., “IMPLEMENTASI METODE DISCRETE WAVELET TRANSFORM (DWT) PADA WATERMARKING CITRA DIGITAL KEASLIAN KARYA BERBASIS WEB,” Coding Jurnal Komputer dan Aplikasi, vol. 10, no. 01, pp. 124–135, May 2022, doi: 10.26418/CODING.V10I01.52736.
[35] N. H. Ja’afar and A. Ahmad, “Pipeline architectures of Three-dimensional Daubechies wavelet transform using hybrid method,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 15, no. 1, pp. 240–246, Jul. 2019, doi: 10.11591/IJEECS.V15.I1.PP240-246.
[36] M. B. Akbar, “Comparison and Analysis several Wavelet from Daubechies Family in Image Compression,” 2020 8th International Conference on Cyber and IT Service Management, CITSM 2020, Oct. 2020, doi: 10.1109/CITSM50537.2020.9268908.
[37] B. M. Salih Hasan and A. M. Abdulazeez, “A Review of Principal Component Analysis Algorithm for Dimensionality Reduction,” Journal of Soft Computing and Data Mining, vol. 02, no. 01, pp. 20–30, 2021, doi: 10.30880/jscdm.2021.02.01.003.
[38] D. K. Choubey, P. Kumar, S. Tripathi, and S. Kumar, “Performance evaluation of classification methods with PCA and PSO for diabetes,” Network Modeling Analysis in Health Informatics and Bioinformatics, vol. 9, no. 1, pp. 1–30, Dec. 2020, doi: 10.1007/S13721-019-0210-8/METRICS.
[39] M. R. Mahmoudi, M. H. Heydari, S. N. Qasem, A. Mosavi, and S. S. Band, “Principal component analysis to study the relations between the spread rates of COVID-19 in high-risk countries,” Alexandria Engineering Journal, vol. 60, no. 1, pp. 457–464, Feb. 2021, doi: 10.1016/J.AEJ.2020.09.013.
[40] Mengying Shu, “Deep Learning for Image Classification on Very Small Datasets Using Transfer Learning,” Iowa State University, 2019.
[41] G. Zhang, J. Kato, Y. Wang, and K. Mase, “How to initialize the CNN for small datasets: Extracting discriminative filters from the pre-trained model,” in 2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR), 2015, pp. 479–483. doi: 10.1109/ACPR.2015.7486549.
[42] T. Kattenborn, J. Leitloff, F. Schiefer, and S. Hinz, “Review on Convolutional Neural Networks (CNN) in vegetation remote sensing,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 173, no. December 2020, pp. 24–49, 2021, doi: 10.1016/j.isprsjprs.2020.12.010.
[43] R. Kurniawan et al., “IMPLEMENTASI ARSITEKTUR XCEPTION PADA MODEL MACHINE LEARNING KLASIFIKASI SAMPAH ANORGANIK,” Jurnal Informatika dan Teknik Elektro Terapan, vol. 11, no. 2, pp. 2830–7062, Apr. 2023, doi: 10.23960/JITET.V11I2.3034.
[44] A. M. Sarhan, “A Novel Lung Cancer Detection Method Using Wavelet Decomposition and Convolutional Neural Network,” Journal of Biomedical Science and Engineering, vol. 13, no. 05, pp. 81–92, May 2020, doi: 10.4236/JBISE.2020.135008.
[45] O. N. Oyelade and A. E. Ezugwu, “A novel wavelet decomposition and transformation convolutional neural network with data augmentation for breast cancer detection using digital mammogram,” Scientific Reports, vol. 12, no. 1, Dec. 2022, doi: 10.1038/S41598-022-09905-3.
[46] H. I. Muhammad, K. I. Musa, M. L. Abdulrahman, A. Abubakar, K. Umar, and A. Ishola, “Enhancing Detection Performance of Face Recognition Algorithm Using PCA-Faster R-CNN,” European Journal of Electrical Engineering and Computer Science, vol. 5, no. 3, pp. 9–16, May 2021, doi: 10.24018/EJECE.2021.5.3.321.
[47] H. Prasada, R. Kunadharaju, N. Sandhya, and R. Mehra, “Detection of Brain Tumor Using Unsupervised Enhanced K-Means, PCA and Supervised SVM Machine Learning Algorithms,” International Research Journal on Advanced Science Hub, vol. 02, no. Special Issue ICSTM 12S, pp. 62–67, Dec. 2020, doi: 10.47392/IRJASH.2020.262.
[48] A. M. Ismael and A. Şengür, “Deep learning approaches for COVID-19 detection based on chest X-ray images,” Expert Systems with Applications, vol. 164, p. 114054, Feb. 2021, doi: 10.1016/J.ESWA.2020.114054.
[49] J. Zhou, M. Xiao, Y. Niu, and G. Ji, “Rolling Bearing Fault Diagnosis Based on WGWOA-VMD-SVM,” Sensors, vol. 22, no. 16, p. 6281, Aug. 2022, doi: 10.3390/S22166281.
[50] A. S. Zamani et al., “Performance of Machine Learning and Image Processing in Plant Leaf Disease Detection,” Journal of Food Quality, vol. 2022, 2022, doi: 10.1155/2022/1598796.
[51] I. Wayan, P. Pratama, A. Kesiman, I. Gede, and A. Gunadi, “Frequency Band and PCA Feature Comparison for EEG Signal Classification,” Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, vol. 12, no. 1, pp. 1–12, Mar. 2021, doi: 10.24843/LKJITI.2021.V12.I01.P01.
[52] I. Putu, B. Gede, P. Raharja, I. Made, S. Putra, and T. Le, “Kekarangan Balinese Carving Classification Using Gabor Convolutional Neural Network,” Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, vol. 13, no. 1, pp. 1–10, Apr. 2022, doi: 10.24843/LKJITI.2022.V13.I01.P01.
[53] G. Ayu, A. Diatri Indradewi, N. Wayan, S. Saraswati, and N. W. Wardani, “COVID-19 Chest X-Ray Detection Performance Through Variations of Wavelets Basis Function,” MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 21, no. 1, pp. 31–42, Nov. 2021, doi: 10.30812/MATRIK.V21I1.1089.
[54] P. Wang, E. Fan, and P. Wang, “Comparative analysis of image classification algorithms based on traditional machine learning and deep learning,” Pattern Recognition Letters, vol. 141, pp. 61–67, 2021, doi: https://doi.org/10.1016/j.patrec.2020.07.042.
[2] R. Kundu, R. Das, Z. W. Geem, G. T. Han, and R. Sarkar, “Pneumonia detection in chest X-ray images using an ensemble of deep learning models,” PLoS One, vol. 16, no. 9, Sep. 2021, doi: 10.1371/JOURNAL.PONE.0256630.
[3] “Fluid Around the Lungs or Malignant Pleural Effusion,” Cancer.Net. Accessed: Feb. 26, 2024. [Online]. Available: https://www.cancer.net/coping-with-cancer/physical-emotional-and-social-effects-cancer/managing-physical-side-effects/fluid-around-lungs-or-malignant-pleural-effusion
[4] M. A. Almaiah et al., “Performance Investigation of Principal Component Analysis for Intrusion Detection System Using Different Support Vector Machine Kernels,” Electronics. 2022, Vol. 11, Page 3571, vol. 11, no. 21, p. 3571, Nov. 2022, doi: 10.3390/ELECTRONICS11213571.
[5] W. Zhou, H. Wang, and Z. Wan, “Ore Image Classification Based on Improved CNN,” Computers and Electrical Engineering, vol. 99, p. 107819, Apr. 2022, doi: 10.1016/J.COMPELECENG.2022.107819.
[6] M. E. Paoletti, J. M. Haut, X. Tao, J. P. Miguel, and A. Plaza, “A New GPU Implementation of Support Vector Machines for Fast Hyperspectral Image Classification,” Remote Sensing, vol. 12, no. 8, p. 1257, Apr. 2020, doi: 10.3390/RS12081257.
[7] C. Tchito Tchapga et al., “Biomedical Image Classification in a Big Data Architecture Using Machine Learning Algorithms,” Journal of Healthcare Engineering, vol. 2021, 2021, doi: 10.1155/2021/9998819.
[8] D. A. Otchere, T. O. Arbi Ganat, R. Gholami, and S. Ridha, “Application of supervised machine learning paradigms in the prediction of petroleum reservoir properties: Comparative analysis of ANN and SVM models,” Journal of Petroleum Science and Engineering, vol. 200, p. 108182, May 2021, doi: 10.1016/J.PETROL.2020.108182.
[9] Q. Yang, H. Zhang, J. Xia, and X. Zhang, “Evaluation of magnetic resonance image segmentation in brain low-grade gliomas using support vector machine and convolutional neural network,” Quantitative Imaging in Medicine and Surgery, vol. 11, no. 1, p. 300, Jan. 2021, doi: 10.21037/QIMS-20-783.
[10] B. Gaye, D. Zhang, and A. Wulamu, “Improvement of Support Vector Machine Algorithm in Big Data Background,” Mathematical Problems in Engineering, vol. 2021, 2021, doi: 10.1155/2021/5594899.
[11] S. Mawarni, “Medical External Wound Image Classification Using Support Vector Machine Technique,” Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika, vol. 9, no. 2, pp. 98–103, 2023.
[12] J. B. A. Das, A. Das, A. Sarangi, D. Mishra, and M. N. Mohanty, “Cancerous image classification using support vector machine with optimized statistical features,” in AIP Conference Proceedings, vol. 2904, no. 1, p. 20010, Oct. 2023, doi: 10.1063/5.0171453.
[13] E. M. Senan and M. E. Jadhav, “Diagnosis of Dermoscopy Images for the Detection of Skin Lesions Using SVM and KNN,” in Proceedings of Third International Conference on Sustainable Computing, R. C. Poonia, V. Singh, D. Singh Jat, M. J. Diván, and M. S. Khan, Eds., Singapore: Springer Nature Singapore, 2022, pp. 125–134.
[14] A. Murugan, S. A. H. Nair, and K. P. S. Kumar, “Detection of Skin Cancer Using SVM, Random Forest and kNN Classifiers,” Journal of Medical Systems, vol. 43, no. 8, 2019, doi: 10.1007/s10916-019-1400-8.
[15] D. Chen et al., “Novel joint algorithm based on EEG in complex scenarios,” Computer Assisted Surgery, vol. 24, no. sup2, pp. 117–125, 2019, doi: 10.1080/24699322.2019.1649078.
[16] F. Liu and M. Brown, Breast cancer recognition by support vector machine combined with Daubechies wavelet transform and principal component analysis, vol. 30. Springer International Publishing, 2019. doi: 10.1007/978-3-030-00665-5_177.
[17] H. P. Raju Kunadharaju, S. N., and R. Mehra, “Detection of Brain Tumor Using Unsupervised Enhanced K-Means, PCA and Supervised SVM Machine Learning Algorithms,” International Research Journal on Advanced Science Hub, vol. 02, no. Special Issue ICSTM 12S, pp. 62–67, 2020, doi: 10.47392/irjash.2020.262.
[18] I. W. P. Pratama, M. W. A. Kesiman, and I. G. A. Gunadi, “Frequency Band and PCA Feature Comparison for EEG Signal Classification,” Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, vol. 12, no. 1, p. 1, 2021, doi: 10.24843/lkjiti.2021.v12.i01.p01.
[19] I. G. A. A. D. Indradewi, N. W. S. Saraswati, and N. W. Wardani, “COVID-19 Chest X-Ray Detection Performance Through Variations of Wavelets Basis Function,” MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 21, no. 1, pp. 31–42, Nov. 2021, doi: 10.30812/matrik.v21i1.1089.
[20] N. W. S. Saraswati, N. W. Wardani, and I. G. A. A. D. Indradewi, “Detection of Covid Chest X-Ray using Wavelet and Support Vector Machines,” International Journal of Engineering and Emerging Technology, vol. 5, no. 2, pp. 116–121, Dec. 2020, doi: 10.24843/IJEET.2020.V05.I02.P019.
[21] D. N. Avianty, P. I. G. P. S. Wijaya, and F. Bimantoro, “The Comparison of SVM and ANN Classifier for COVID-19 Prediction,” Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, vol. 13, no. 2, p. 128, 2022, doi: 10.24843/lkjiti.2022.v13.i02.p06.
[22] I. P. B. G. Prasetyo Raharja, I. M. Suwija Putra, and T. Le, “Kekarangan Balinese Carving Classification Using Gabor Convolutional Neural Network,” Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, vol. 13, no. 1, p. 1, 2022, doi: 10.24843/lkjiti.2022.v13.i01.p01.
[23] K. K. Gv and G. M. Reddy, “Automatic classification of whole slide pap smear images using CNN with PCA based feature interpretation,” IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, vol. 2019-June, pp. 1074–1079, 2019, doi: 10.1109/CVPRW.2019.00140.
[24] H. Basak, R. Kundu, S. Chakraborty, and N. Das, “Cervical Cytology Classification Using PCA and GWO Enhanced Deep Features Selection,” SN Computer Science, vol. 2, no. 5, 2021, doi: 10.1007/s42979-021-00741-2.
[25] M. Ahmadi, A. Sharifi, M. Jafarian Fard, and N. Soleimani, “Detection of brain lesion location in MRI images using convolutional neural network and robust PCA,” International Journal of Neuroscience, vol. 133, no. 1, pp. 55–66, 2023, doi: 10.1080/00207454.2021.1883602.
[26] Priyanka and D. Kumar, “Feature Extraction and Selection of kidney Ultrasound Images Using GLCM and PCA,” Procedia Computer Science, vol. 167, pp. 1722–1731, 2020, doi: https://doi.org/10.1016/j.procs.2020.03.382.
[27] R. Sharma and A. Singh, “An Integrated Approach towards Efficient Image Classification Using Deep CNN with Transfer Learning and PCA,” Advances in Technology Innovation, vol. 7, no. 2, pp. 105–117, 2022, doi: 10.46604/aiti.2022.8538.
[28] M. Wulandari, Basari, and D. Gunawan, “Evaluation of wavelet transform preprocessing with deep learning aimed at palm vein recognition application,” AIP Conference Proceedings, vol. 2193, no. 1, p. 50005, Dec. 2019, doi: 10.1063/1.5139378.
[29] H. Hasan, H. Z. M. Shafri, and M. Habshi, “A Comparison between Support Vector Machine (SVM) and Convolutional Neural Network (CNN) Models for Hyperspectral Image Classification,” IOP Conference Series: Earth and Environmental Science, vol. 357, no. 1, 2019, doi: 10.1088/1755-1315/357/1/012035.
[30] D. Anggriandi, E. Utami, and D. Ariatmanto, “Comparative Analysis of CNN and CNN-SVM Methods For Classification Types of Human Skin Disease,” Sinkron : Jurnal dan Penelitian Teknik Informatika, vol. 8, no. 4, pp. 2168–2178, 2023.
[31] A. Peryanto, A. Yudhana, and R. Umar, “Convolutional Neural Network and Support Vector Machine in Classification of Flower Images,” Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika, vol. 8, no. 1, pp. 1–7, Mar. 2022, doi: 10.23917/KHIF.V8I1.15531.
[32] N. Wayan et al., “Recognizing Pneumonia Infection in Chest X-Ray Using Deep Learning,” MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 23, no. 1, pp. 17–28, Oct. 2023, doi: 10.30812/MATRIK.V23I1.3197.
[33] J. P. Cohen, P. Morrison, L. Dao, K. Roth, T. Q. Duong, and M. Ghassemi, “COVID-19 Image Data Collection: Prospective Predictions Are the Future,” pp. 1–38, 2020.
[34] M. Utami et al., “IMPLEMENTASI METODE DISCRETE WAVELET TRANSFORM (DWT) PADA WATERMARKING CITRA DIGITAL KEASLIAN KARYA BERBASIS WEB,” Coding Jurnal Komputer dan Aplikasi, vol. 10, no. 01, pp. 124–135, May 2022, doi: 10.26418/CODING.V10I01.52736.
[35] N. H. Ja’afar and A. Ahmad, “Pipeline architectures of Three-dimensional Daubechies wavelet transform using hybrid method,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 15, no. 1, pp. 240–246, Jul. 2019, doi: 10.11591/IJEECS.V15.I1.PP240-246.
[36] M. B. Akbar, “Comparison and Analysis several Wavelet from Daubechies Family in Image Compression,” 2020 8th International Conference on Cyber and IT Service Management, CITSM 2020, Oct. 2020, doi: 10.1109/CITSM50537.2020.9268908.
[37] B. M. Salih Hasan and A. M. Abdulazeez, “A Review of Principal Component Analysis Algorithm for Dimensionality Reduction,” Journal of Soft Computing and Data Mining, vol. 02, no. 01, pp. 20–30, 2021, doi: 10.30880/jscdm.2021.02.01.003.
[38] D. K. Choubey, P. Kumar, S. Tripathi, and S. Kumar, “Performance evaluation of classification methods with PCA and PSO for diabetes,” Network Modeling Analysis in Health Informatics and Bioinformatics, vol. 9, no. 1, pp. 1–30, Dec. 2020, doi: 10.1007/S13721-019-0210-8/METRICS.
[39] M. R. Mahmoudi, M. H. Heydari, S. N. Qasem, A. Mosavi, and S. S. Band, “Principal component analysis to study the relations between the spread rates of COVID-19 in high-risk countries,” Alexandria Engineering Journal, vol. 60, no. 1, pp. 457–464, Feb. 2021, doi: 10.1016/J.AEJ.2020.09.013.
[40] Mengying Shu, “Deep Learning for Image Classification on Very Small Datasets Using Transfer Learning,” Iowa State University, 2019.
[41] G. Zhang, J. Kato, Y. Wang, and K. Mase, “How to initialize the CNN for small datasets: Extracting discriminative filters from the pre-trained model,” in 2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR), 2015, pp. 479–483. doi: 10.1109/ACPR.2015.7486549.
[42] T. Kattenborn, J. Leitloff, F. Schiefer, and S. Hinz, “Review on Convolutional Neural Networks (CNN) in vegetation remote sensing,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 173, no. December 2020, pp. 24–49, 2021, doi: 10.1016/j.isprsjprs.2020.12.010.
[43] R. Kurniawan et al., “IMPLEMENTASI ARSITEKTUR XCEPTION PADA MODEL MACHINE LEARNING KLASIFIKASI SAMPAH ANORGANIK,” Jurnal Informatika dan Teknik Elektro Terapan, vol. 11, no. 2, pp. 2830–7062, Apr. 2023, doi: 10.23960/JITET.V11I2.3034.
[44] A. M. Sarhan, “A Novel Lung Cancer Detection Method Using Wavelet Decomposition and Convolutional Neural Network,” Journal of Biomedical Science and Engineering, vol. 13, no. 05, pp. 81–92, May 2020, doi: 10.4236/JBISE.2020.135008.
[45] O. N. Oyelade and A. E. Ezugwu, “A novel wavelet decomposition and transformation convolutional neural network with data augmentation for breast cancer detection using digital mammogram,” Scientific Reports, vol. 12, no. 1, Dec. 2022, doi: 10.1038/S41598-022-09905-3.
[46] H. I. Muhammad, K. I. Musa, M. L. Abdulrahman, A. Abubakar, K. Umar, and A. Ishola, “Enhancing Detection Performance of Face Recognition Algorithm Using PCA-Faster R-CNN,” European Journal of Electrical Engineering and Computer Science, vol. 5, no. 3, pp. 9–16, May 2021, doi: 10.24018/EJECE.2021.5.3.321.
[47] H. Prasada, R. Kunadharaju, N. Sandhya, and R. Mehra, “Detection of Brain Tumor Using Unsupervised Enhanced K-Means, PCA and Supervised SVM Machine Learning Algorithms,” International Research Journal on Advanced Science Hub, vol. 02, no. Special Issue ICSTM 12S, pp. 62–67, Dec. 2020, doi: 10.47392/IRJASH.2020.262.
[48] A. M. Ismael and A. Şengür, “Deep learning approaches for COVID-19 detection based on chest X-ray images,” Expert Systems with Applications, vol. 164, p. 114054, Feb. 2021, doi: 10.1016/J.ESWA.2020.114054.
[49] J. Zhou, M. Xiao, Y. Niu, and G. Ji, “Rolling Bearing Fault Diagnosis Based on WGWOA-VMD-SVM,” Sensors, vol. 22, no. 16, p. 6281, Aug. 2022, doi: 10.3390/S22166281.
[50] A. S. Zamani et al., “Performance of Machine Learning and Image Processing in Plant Leaf Disease Detection,” Journal of Food Quality, vol. 2022, 2022, doi: 10.1155/2022/1598796.
[51] I. Wayan, P. Pratama, A. Kesiman, I. Gede, and A. Gunadi, “Frequency Band and PCA Feature Comparison for EEG Signal Classification,” Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, vol. 12, no. 1, pp. 1–12, Mar. 2021, doi: 10.24843/LKJITI.2021.V12.I01.P01.
[52] I. Putu, B. Gede, P. Raharja, I. Made, S. Putra, and T. Le, “Kekarangan Balinese Carving Classification Using Gabor Convolutional Neural Network,” Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, vol. 13, no. 1, pp. 1–10, Apr. 2022, doi: 10.24843/LKJITI.2022.V13.I01.P01.
[53] G. Ayu, A. Diatri Indradewi, N. Wayan, S. Saraswati, and N. W. Wardani, “COVID-19 Chest X-Ray Detection Performance Through Variations of Wavelets Basis Function,” MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 21, no. 1, pp. 31–42, Nov. 2021, doi: 10.30812/MATRIK.V21I1.1089.
[54] P. Wang, E. Fan, and P. Wang, “Comparative analysis of image classification algorithms based on traditional machine learning and deep learning,” Pattern Recognition Letters, vol. 141, pp. 61–67, 2021, doi: https://doi.org/10.1016/j.patrec.2020.07.042.
Published
2024-03-25
How to Cite
SARASWATI, Ni Wayan Sumartini; DEWI, Dewa Ayu Putu Rasmika; PIROZMAND, Poria.
Comparative Analysis of SVM and CNN for Pneumonia Detection in Chest X-Ray.
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, [S.l.], v. 15, n. 1, p. 38-50, mar. 2024.
ISSN 2541-5832.
Available at: <https://ojs.unud.ac.id/index.php/lontar/article/view/110005>. Date accessed: 21 nov. 2024.
doi: https://doi.org/10.24843/LKJITI.2024.v15.i01.p04.
Section
Articles
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