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CLASSIFICATION OF K-NEAREST NEIGHBOR (K-NN) AND CONVOLUTIONAL NEURAL NETWORK (CNN) FOR THE IDENTIFICATION OF BRONCHITIS DISEASE IN TODDLERS USING GLCM FEATURE EXTRACTION BASED ON THORAX X-RAY IMAGES

M. Fachrurrozi Nasution(1*), Wanayumini Wanayumini(2), Rika Roesnelly(3)
(1) Universitas Potensi Utama, North Sumatra, Indonesia
(2) Universitas Potensi Utama, North Sumatra, Indonesia
(3) Universitas Potensi Utama, North Sumatra, Indonesia
(*) Corresponding Author

Abstract

K-Nearest Neighbor (K-NN) is a classification method that seeks the majority class from the k-nearest neighbors of a sample to be classified. Meanwhile, Convolutional Neural Network (CNN) is a type of artificial neural network specifically designed to recognize patterns in image data. The features are then extracted using GLCM (Gray Level Co-occurrence Matrix) from Thorax X-Ray images. This research aims to develop two classification approaches, namely K-Nearest Neighbor (K-NN) and Convolutional Neural Network (CNN), to detect bronchitis disease in toddlers based on Thorax X-Ray images. Feature extraction based on the Gray Level Co-occurrence Matrix (GLCM) is used to transform images into numerical features that can be processed by classification algorithms. The results from both methods will be combined based on various evaluation metrics, such as accuracy, precision, recall, F1-score, etc

References


Arrofiqoh, E. N., & Harintaka, H. (2018). Implementasi metode convolutional neural network untuk klasifikasi tanaman pada citra resolusi tinggi. Geomatika, 24(2), 61.

Balamurali, B. T., Hee, H. I., Kapoor, S., Teoh, O. H., Teng, S. S., Lee, K. P., Herremans, D., & Chen, J. M. (2021). Deep neural network-based respiratory pathology classification using cough sounds. Sensors, 21(16), 5555.

Chen, H., & Ye, W. (2019). Classification of human activity based on radar signal using 1-D convolutional neural network. IEEE Geoscience and Remote Sensing Letters, 17(7), 1178–1182.

Gazalba, I., & Reza, N. G. I. (2017). Comparative analysis of k-nearest neighbor and modified k-nearest neighbor algorithm for data classification. 2017 2nd International Conferences on Information Technology, Information Systems and Electrical Engineering (ICITISEE), 294–298.

GM, H., Gourisaria, M. K., Rautaray, S. S., & Pandey, M. (2021). Pneumonia detection using CNN through chest X-ray. Journal of Engineering Science and Technology (JESTEC), 16(1), 861–876.

Guo, G., Wang, H., Bell, D., Bi, Y., & Greer, K. (2003). KNN model-based approach in classification. On The Move to Meaningful Internet Systems 2003: CoopIS, DOA, and ODBASE: OTM Confederated International Conferences, CoopIS, DOA, and ODBASE 2003, Catania, Sicily, Italy, November 3-7, 2003. Proceedings, 986–996.

Hidayati, N., & Hermawan, A. (2021). K-Nearest Neighbor (K-NN) algorithm with Euclidean and Manhattan in classification of student graduation. Journal of Engineering and Applied Technology, 2(2), 86–91.

Imiliati, C., Nugroho, N. B., & Syaifuddin, M. (2021). SISTEM PAKAR DALAM MENDIAGNOSA PENYAKIT BRONCHITIS PADA ANAK MENGGUNAKAN METODE CERTAINTY FACTOR DAN TEOREMA BAYES DI UPT. RSKPARU PROVENSI SUMATERA UTARA. Jurnal Cyber Tech, 3(7).

Isnain, A. R., Supriyanto, J., & Kharisma, M. P. (2021). Implementation of K-Nearest Neighbor (K-NN) Algorithm For Public Sentiment Analysis of Online Learning. IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 15(2), 121–130.

Jayalakshmy, S., Priya, B. L., & Kavya, N. (2020). CNN based Categorization of respiratory sounds using spectral descriptors. 2020 International Conference on Communication, Computing and Industry 4.0 (C2I4), 1–5.

Komalasari, R. (2022). Pemanfaatan Kecerdasan Buatan (Ai) Dalam Telemedicine: Dari Perspektif Profesional Kesehatan. Jurnal Kedokteran Mulawarman, 9(2), 72–81.

Naufal, M. F., Kusuma, S. F., Tanus, K. C., Sukiwun, R. V., Kristiano, J., & Lieyanto, J. O. (2021). Analisis Perbandingan Algoritma Klasifikasi Citra Chest X-ray Untuk Deteksi Covid-19. Teknika, 10(2), 96–103.

Prasetyo, B. D. (2020). Klasifikasi Citra X-Ray Paru-paru Anak Pneumonia dan Non-Pneumonia Menggunakan Metode Segmentasi dan Deteksi Tepi.

Rahim, A., Luthfi, E. T., & Learning, D. (2019). Convolutional Neural Network Untuk Kalasifikasi.

Sapata, B. M., & Juniati, D. (2019). Klasifikasi Penyakit Paru Berdasarkan Citra X-Ray Thorax Menggunakan Metode Fraktal Box Counting. MATHunesa: Jurnal Ilmiah Matematika, 7(3), 228–235.

Srivastava, A., Jain, S., Miranda, R., Patil, S., Pandya, S., & Kotecha, K. (2021). Deep learning based respiratory sound analysis for detection of chronic obstructive pulmonary disease. PeerJ Computer Science, 7, e369.

Surya, R. A., Fadlil, A., & Yudhana, A. (2017). Ekstraksi ciri metode Gray Level Co-Occurrence Matrix (GLCM) dan Filter Gabor untuk klasifikasi citra batik pekalongan. Jurnal Informatika: Jurnal Pengembangan IT, 2(2), 23–26


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