Klasifikasi Citra X-Ray Covid-19 Menggunakan Three-layered CNN Model

Handoko, Aaron Berliano; Timotius, Ivanna K.; Utomo, Darmawan

doi:10.31358/techne.v21i2.316

Cited by 1 publication

(2 citation statements)

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“…Research using the same model was carried out by [32] by investing the effect of epoch on finding the best hyperparameters for several epoch experiments to obtain an accuracy of 95% on train data and 78% on test data. The accuracy value obtained by [33] averaged 96% on test data using other research.…”

Section: Introductionmentioning

confidence: 61%

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MobileNet Classifier for Detecting Chest X-Ray Images of COVID-19 based on Convolutional Neural Network

Ghani,

Intan,

Rizal

2023

Ilk. J. Ilm.

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Since the COVID-19 pandemic occurred all over the world, numerous studies were carried out to overcome this problem, including COVID-19 image analysis. An expert analysis based on the Chest X-ray images of COVID-19 determines the progression of the lung condition. Eye visualization and expertise of a radiologist have limitations in handling big cases. This study aims to implement the Convolutional Neural Network (CNN) and MobileNet models as deep learning models to classify chest X-ray images into multiclassification, three categories: COVID-19, normal, and virus. The processes were pre-processing and processing. The pre-processing stage was preparing data, and the processing stage was the implementation model and investigating the best model performance in both convolution and classification in depth-wise convolution and batch normalization. The metrics were accuracy, precision, f1-score, and recall. The CNN results of accuracy, precision, recall, and f1-score respectively were 0.94; 0.99; 0.95; and 0.96. The MobileNet results of the metrics were 0.97; 0.98; 0.99, and 0.99. The MobileNet outperforms the CNN results due to depth-wise convolution and batch normalization. Both models contribute to the faster epoch of the best hyperparameter to achieve loss and accuracy convergence. The models are worth recommending to deployment front-end.

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Section: Introductionmentioning

confidence: 61%

“…This gives MobileNet the ability to recognize images more validly than CNN. In addition, because of batch normalization, even though MobileNet has Based on these results, the investigation of model performance on CXR image objects outperformed previous authors, including [32], [33], [34], [36].…”

Section: Evaluation Processmentioning

confidence: 72%