CLASSIFICATION OF COVID 19, PNEUMONIA AND NORMAL LUNGS BASED ON X-RAY IMAGES USING CONVOLUTIONAL NEURAL NETWORK
Abstract
The RT-PCR (Real Time – Polymerase Chain Reaction) examination method is a type of Nucleic Acid Amplification Test (NAAT) method currently used by hospitals, laboratories and other facilities stipulated by the Minister of Health as the main standard for the diagnosis of Covid-19. It is this sensitivity and specificity in detecting genetic material that makes the PCR method quite important and is still the gold standard in detecting SARS-CoV. Although considered the best, not without flaws. The RT-PCR examination method requires two processes, namely extraction and amplification. which takes several days to find out the results of the RT-PCR examination. However, the positive rate for this method is reported to be around 30-60%, so there are still patients who are not diagnosed and can cause infection in healthy people. One of the alternatives used for the detection of Covid-19 is Chest Radiographic Imaging (X-Ray or Computed Tomography Scan) which is a tool that is often used periodically as a tool to easily and quickly diagnose pneumonia and Covid-19. The detection of Covid-19 by X-Ray Imagery has proven to be feasible as well as deep residual tissue studies. Detection of Covid-19 Disease Based on X-Ray Imagery and Results of Implementation of Corona Virus Detection on X-Ray Imagery Using Intelligence Algorithms. In this study a classification system for Covid-19, Pneumonia and Normal Lung will be designed using the CNN architectural model which uses three dataset sources to add (combine) Pneumonia, Normal Lung image data, and also Covid-19 image data. so that it becomes one dataset. which aims to analyze the level of accuracy in the proposed model.
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DOI: https://doi.org/10.33365/jti.v18i1.2688
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