KLASIFIKASI CITRA X-RAY COVID-19 DENGAN MODEL CONVOLUTIONAL NEURAL NETWORKS ALGORITHMA LOGISTIC REGRETION

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  • 08 Aug
  • 2022

KLASIFIKASI CITRA X-RAY COVID-19 DENGAN MODEL CONVOLUTIONAL NEURAL NETWORKS ALGORITHMA LOGISTIC REGRETION

Covid-19 (Corona Virus Disease) a new infectious outbreak originating from Wuhan in
2019. To find out how to diagnose COVID-19 is to analyze X-ray images of the lungs. Medical experts
analyze the x-ray images of the lungs to determine the diagnosis, whether it is Covid or normal. This is
very time consuming and inefficient. Therefore, technology is needed that can quickly diagnose the
disease. Convolutional Neural Network (CNN) with Logistic Regression algorithm is one of the
developments of the Multilayer Perceptron (MLP) algorithm which is designed to identify various image
patterns from various sides. The CNN model built in this study has 200 convolution layers with ReLU
activation functions, Batch Normalization, and 5 max-pooling layers. The classification layer of the
CNN model built applies global average pooling which produces 2012 neurons that are directly
connected to the last layer with the inception-v3 function. The accuracy of the results of the CNN
model that was built succeeded in achieving an overall accuracy of 92.14% which was tested using
200 image data. The conclusion of this study is that the Convolutional Neural Network (CNN)
algorithm that was built is relatively capable of diagnosing COVID-19 disease based on X-ray images
of the lungs and the effectiveness of the model for diagnosing COVID-19 is lower than diagnosing
diseases that are not infected with COVID-19.

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