Klasifikasi Penyakit Paru-Paru Berdasarkan Peningkatan Kualitas Kontras dan EfficientNet Menggunakan Gambar X-Ray
Abstract
COVID-19 dan penyakit paru-paru telah menjadi faktor utama penyebab kematian manusia di seluruh dunia. Kematian pasien dipengaruhi oleh keterlambatan deteksi dini. Sebagian besar profesional medis menggunakan gambar untuk mengidentifikasi kondisi paru-paru. Namun, para ahli yang dapat me-diagnosis dengan gambar sangat terbatas. Diagnosis gambar mendiagnosa menggunakan penglihatan manusia secara konvensional. Klasifikasi penyakit paru-paru sangat bervariasi. Masalah yang disebutkan di atas menunjukkan bahwa deteksi penyakit paru-paru dengan Artificial Intelligence (AI) yang efektif telah ditetapkan. Namun, sebagian besar hasil penyakit paru-paru salah didiagnosis. Bagi pasien, masalah ini menjadi masalah besar. Bertujuan untuk menangani klasifikasi penyakit paru-paru dengan deteksi kesalahan yang tinggi, kami menggunakan beberapa teknik pre-processing gambar dan menerapkan model pembelajaran mendalam dalam EfficientNet. Model Pre-processing termasuk augmentasi, peningkatan white balance, dan peningkatan kontras. Berdasarkan penelitian sebelumnya, mayoritas proses analisa gambar medis mengalami kualitas gambar yang rendah. Berdasarkan laporan eksperimen, model yang kami usulkan mencapai hasil yang signifikan dalam mengurangi kesalahan deteksi pada klasifikasi penyakit paru-paru. Dimana hasil F1 score-nya 0,97, recallnya 0,98, presisinya 0,96, dan akurasinya 0,97. Kami mempertimbangkan untuk menggunakan model yang kami usulkan dalam klasifikasi multi-class. Kami mengevaluasi model yang kami usulkan menggunakan evaluation metric dan AUC Curve.
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References
S. Kumar, O. Ivanova, A. Melyokhin, and P. Tiwari, “Deep-learning-enabled multimodal data fusion for lung disease classification,” Inform Med Unlocked, vol. 42, Jan. 2023, doi: 10.1016/j.imu.2023.101367.
M. Jasmine Pemeena Priyadarsini et al., “Lung Diseases Detection Using Various Deep Learning Algorithms,” J Healthc Eng, vol. 2023, 2023, doi: 10.1155/2023/3563696.
Hanafi, A. Pranolo, and Y. Mao, “Cae-covidx: Automatic covid-19 disease detection based on x-ray images using enhanced deep convolutional and autoencoder,” International Journal of Advances in Intelligent Informatics, vol. 7, no. 1, pp. 49–62, 2021, doi: 10.26555/ijain.v7i1.577.
Hanafi and B. M. Aboobaider, “Word Sequential Using Deep LSTM and Matrix Factorization to Handle Rating Sparse Data for E-Commerce Recommender System,” Comput Intell Neurosci, vol. 2021, no. 1, 2021, doi: https://doi.org/10.1155/2021/8751173 Research.
Hanafi, N. Suryana, and A. S. B. H. Basari, “Convolutional-NN and word embedding for making an effective product recommendation based on enhanced contextual understanding of a product review,” Int J Adv Sci Eng Inf Technol, vol. 9, no. 3, 2019, doi: 10.18517/ijaseit.9.3.8843.
Hanafi, N. Suryana, and A. Samad, “Dynamic convolutional neural network for eliminating item sparse data on recommender system,” IJAIN, vol. 4, no. 3, pp. 226–237, 2018.
V. Ravi, H. Narasimhan, C. Chakraborty, and T. D. Pham, “Deep learning-based meta-classifier approach for COVID-19 classification using CT scan and chest X-ray images,” Multimed Syst, vol. 28, no. 4, pp. 1401–1415, 2022.
S. T. H. Kieu, A. Bade, M. H. A. Hijazi, and H. Kolivand, “A survey of deep learning for lung disease detection on medical images: state-of-the-art, taxonomy, issues and future directions,” J Imaging, vol. 6, no. 12, p. 131, 2020.
Z. A. Varzaneh, A. Orooji, L. Erfannia, and M. Shanbehzadeh, “A new COVID-19 intubation prediction strategy using an intelligent feature selection and K-NN method,” Inform Med Unlocked, vol. 28, p. 100825, 2022, doi: 10.1016/j.imu.2021.100825.
H. Malik and T. Anees, “BDCNet: Multi-classification convolutional neural network model for classification of COVID-19, pneumonia, and lung cancer from chest radiographs,” Multimed Syst, vol. 28, no. 3, pp. 815–829, 2022.
A. M. Ayalew, A. O. Salau, Y. Tamyalew, B. T. Abeje, and N. Woreta, “X-Ray image-based COVID-19 detection using deep learning,” Multimed Tools Appl, pp. 44507–44525, 2023, doi: 10.1007/s11042-023-15389-8.
R. Prince, Z. Niu, Z. Y. Khan, M. Emmanuel, and N. Patrick, “COVID-19 detection from chest X-ray images using CLAHE-YCrCb, LBP, and machine learning algorithms,” BMC Bioinformatics, vol. 25, no. 1, pp. 1–25, 2024, doi: 10.1186/s12859-023-05427-5.
R. Gulakala, B. Markert, and M. Stoffel, “Rapid diagnosis of Covid-19 infections by a progressively growing GAN and CNN optimisation,” Comput Methods Programs Biomed, vol. 229, p. 107262, 2023.
A. S. Althenayan et al., “COVID-19 Hierarchical Classification Using a Deep Learning Multi-Modal,” Sensors, vol. 24, no. 8, 2024, doi: 10.3390/s24082641.
Y. Liu, W. Xing, M. Zhao, and M. Lin, “An end-to-end framework for diagnosing COVID-19 pneumonia via Parallel Recursive MLP module and Bi-LTSM correlation,”.
2023M. Nishio et al., “Deep learning model for the automatic classification of COVID-19 pneumonia, non-COVID-19 pneumonia, and the healthy: a multi-center retrospective study,” Sci Rep, vol. 12, no. 1, p. 8214, 2022.
M. Siddharta, "Covid CXR Image | Kaggle," [Online]. Available: https://www.kaggle.com/datasets/sid321axn/covid-cxr-image-dataset-research/discussion?sort=undefined [Accessed April 2024]
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