Diagnosa Tuberculosis Paru Berbasis Citra X-ray Menggunakan Convolutional Neural Network

Saeful Bahri, Rusda Wajhillah, Miftah Farid Adiwisastra

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ABSTRAK

Tuberkulosis merupakan sebuah penyakit yang disebabkan oleh Mycobacterium tuberculosis. Penyakit ini dapat menyerang darah, tulang otak dan paru-paru. Diagnosa yang cepat dan akurat sangat diperlukan agar dapat dilakukan pengobatan yang tepat. Diagnosa biasanya dilakukan dengan cara melihat hasil citra x-ray thorax dan hasil test BTA pada pasien. Penelitian ini bertujuan untuk mempercepat identifikasi dalam proses diagnosa dari citra paru yang terinfeksi bakteri tuberculosis. Diagnosa dilakukan dengan bantuan machine learning  berdasarkan hasil citra x-ray menggunakan Algoritma CNN, dengan cara mengklasifikasikan citra x-ray normal dan citra x-ray tuberculosis. Dalam penelitian ini, dibahas tentang penggunaan citra x-ray paru atau citra thorax untuk mendeteksi diagnosa penyakit paru yang disebabkan oleh microbacterium tuberculosis. Hasil klasifikasi dengan CNN yang didapat setelah proses evaluasi model menunjukan nilai yang cukup baik yaitu untuk nilai akurasi di kisaran 89%, sementara untuk nilai f1-score 0,89 

ABSTRACTS

Tuberculosis is a disesase caused by microbacterium tuberculosis. This disease can attack the blood, bones, barain and lungs. A fast and accurate diagnosis is nedded so that appropriate treatment can be carried out. In case tuberculosis, diagnostic is usually done by the result of the chest x-ray image, and the diagnostic result by laboratories BTA test on the patient. This research aims to accelerated the identification in the diagnostic process of lungs image infeted tuberculosis bactria diagnosis is done with the help of machine learning based on the results of x-ray images using the CNN Algorithm, by classifying normal x-ray images and tuberculosis x-ray images. In this study, we discussed the use of the thorax image for diagnostic disease lungs caused by microbacterium tuberculosis. The classification results with CNN obtained after the model evaluation process showed a fairly good value, namely for the accuracy value in the range of 89%, while for the f1-score value of 0,89.


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Referensi


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DOI: https://doi.org/10.31294/ijcit.v6i2.11844

P-ISSN: 2527-449X E-ISSN: 2549-7421
Statistik Pengunjung Jurnal IJCIT
 

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