Classification of Tuberculosis Based on Lung X-Ray Image With Data Science Approach Using Convolutional Neural Network

Authors

  • Mawaddah Harahap Universitas Prima Indonesia, Indonesia
  • Alfeus P. S. Pasaribu Universitas Prima Indonesia, Indonesia
  • Dedy Ridoly Sinaga Universitas Prima Indonesia, Indonesia
  • Romulus Sipangkar Universitas Prima Indonesia, Indonesia
  • Samuel Universitas Prima Indonesia, Indonesia

DOI:

10.33395/sinkron.v7i4.11711

Keywords:

Tuberclosis, X-Ray Image, Lung, Data Science, CNN

Abstract

Tuberculosis (TB) is a potentially serious infectious disease in the lungs, becoming 1 of 10 causes of death. In Indonesia, the disease is ranked third after India and China with 824,000 cases and 93,000 deaths per year, equivalent to 11 deaths per hour. The increasing number of infections and deaths from TB disease is recorded as a result of its transmission, lack of early diagnosis, and inadequate professional radiologists in developing areas where TB is more common. Rapid and accurate diagnosis is essential for appropriate treatment to be initiated. Diagnosis is usually done by looking at the results of the x-ray image of the thorax and the results of the BTA test on the patient. To classify lung x-ray images detected tuberculosis or not, a study was carried out using the Convolutional Neural Network (CNN) method. The test results produce the last epochs value of 200, the accuracy obtained is 0.9892, which means the CNN accuracy is 98%, with validation the accuracy obtained is 0.9835 or 98%. So the results of the classification test using CNN are quite accurate. With the acquisition of CNN results which is quite high, it can be used as a consideration to be used in classifying TB disease.

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How to Cite

Harahap, M., Pasaribu, A. P. S. ., Sinaga, D. R., Sipangkar, R., & Samuel, S. (2022). Classification of Tuberculosis Based on Lung X-Ray Image With Data Science Approach Using Convolutional Neural Network. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 7(4), 2193-2197. https://doi.org/10.33395/sinkron.v7i4.11711

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