Classification SARS-CoV-2 Disease based on CT-Scan Image Using Convolutional Neural Network

Kohsasih, Kelvin Leonardi; Hayadi, B. Herawan

doi:10.15294/sji.v9i2.36583

Cited by 5 publications

(4 citation statements)

References 27 publications

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“…𝑓(𝑥 𝑖 ) = 𝑒 (𝑥 𝑖 ) ∑ 𝑒 𝑥𝑗 𝑘 𝑗=1 (10) Apart from the activation function, DL also uses an optimizer to update the weights to minimize the loss function value and improve the prediction results from the model. Adaptive Moment Estimation (ADAM) is an example of an optimizer that can be used and has lower computation than other optimizers, Equation 11shows the calculation of the updated weights from ADAM.…”

Section: Figure 6 Pooling Layer Typesmentioning

confidence: 99%

“…Research by Triyadi et al A study to classify cataracts using CNN with the VGG-19 and ResNet50 architectures, the result is 91.06% for VGG-19 and 93.5% for ResNet50 [9]. According to [10], their study used CT-Scan image to classify SARS-CoV-2 using ResNet50 architecture and produces an accuracy of 95%. Based on previous research, CNN has proven its ability to classify images so that it can also classify ASL alphabets.…”

Section: Introductionmentioning

confidence: 99%

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Alphabet Classification of Sign System Using Convolutional Neural Network with Contrast Limited Adaptive Histogram Equalization and Canny Edge Detection

Raharjo,

Sugiharti

2023

SJI

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Purpose: There are deaf people who have problems in communicating orally because they do not have the ability to speak and hear. The sign system is used as a solution to this problem, but not everyone understands the use and meaning of the sign system, even in terms of the alphabet. Therefore, it is necessary to classify a sign system in the form of American Sign Language (ASL) using Artificial Intelligence technology to get good results.Methods: This research focuses on improving the accuracy of ASL alphabet classification using the VGG-19 and ResNet50 architecture of the Convolutional Neural Network (CNN) method combined with Contrast Limited Adaptive Histogram Equalization (CLAHE) to improve the detail quality of images and Canny Edge Detection to produce images that focus on the objects in it. The focused result is the accuracy value. This study uses the ASL alphabet dataset from Kaggle.Result: Based on the test results, there are three best accuracy results. The first is using the ResNet50 architecture, CLAHE, and an image size of 128 x 128 pixels with an accuracy of 99.9%, followed by the ResNet50 architecture, CLAHE + Canny Edge Detection, and an image size of 128 x 128 pixels with an accuracy of 99.82 %, and in third place are the VGG-19 architecture, CLAHE, and an image size of 128 x 128 pixels with an accuracy of 98.93%.Novelty: The novelty of this study is the increase in the accuracy value of ASL image classification from previous studies.

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Section: Figure 6 Pooling Layer Typesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Alphabet Classification of Sign System Using Convolutional Neural Network with Contrast Limited Adaptive Histogram Equalization and Canny Edge Detection

Raharjo,

Sugiharti

2023

SJI

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“…Deep learning is a form of development of machine learning [9]. Sarker [10], argues in the current era, deep learning is one of the popular topics to be discussed, especially in the fields of Artificial Intelligence, Machine Learning and data analysis.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Javanese Script Classification with GoogleNet, DenseNet, ResNet, VGG16 and VGG19

Susanto,

Sari,

Rachmawanto

et al. 2024

SJI

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Purpose: Javanese script is a legacy of heritage or heritage in Indonesia originating from the island of Java needs to be preserved. Therefore, in this study, the classification and identification process of Javanese script letters will be carried out using the CNN method. The purpose of this research is to be able to build a model which can properly classify Javanese script, it can help in the process of recognizing letters in Javanese script easily.Methods: In this study, the Javanese script classification process has been used the transfer learning process of Convolutional Neural Network, namely GoogleNet, DenseNet, ResNet, VGG16 and VGG19. The purpose of using transfer learning is to improve the sequential CNN model, processing can be better and optimal because it utilizes a previously trained model.Result: The results obtained after testing in this study are using the transfer learning method, the GoogleNet model gets an accuracy of 88.75%, the DenseNet model gets an accuracy of 92%, the ResNet model gets an accuracy of 82.75%, the VGG16 model gets an accuracy of 99.25% and the VGG19 model gets an accuracy of 99.50%.Novelty: In previous studies, it is still very rare to discuss the Javanese script classification process using the CNN transfer learning method and which method is the most optimal for performing the Javanese script classification process. In this study, it had been resulted find an effective method to be able to carry out the Javanese script classification process properly and optimally.

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“…One of the advanced technologies remembered in the field of computer vision is face recognition. Face recognition is used in several areas, such as attendance systems [2], security detection [3], detection of drowsiness and fatigue [4], crimes from video surveillance [5], robotics [6], biometric security [7], deforestation early detection [8], CT scan images [9], and also SARS-CoV 2 disease [10].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Detection of Face Mask Using Convolutional Neural Network

Imam Husni Al Amin,

Deva Ega Marinda,

Edy Winarno

et al. 2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Masks are a simple barrier that can help us prevent transmission and spread of disease from other people who enter the body, avoid exposure to air pollution, and protect the face from the adverse effects of sunlight. However, many people are still ignorant about the importance of wearing masks for health. This study aims to detect whether or not to use masks in real-time by proposing a deep learning model to reduce illness and death caused by air pollution. The convolutional Neural Network (CNN) method was used in this research to detect facial recognition using a mask and not using a mask. The public dataset used in this research consists of 1300 images with 650 data using masks and 650 data without masks. The results of this study show that the proposed CNN method works well in detecting masked and non-masked faces in real time. The proposed method obtains an accuracy value of 97.5% at epoch 50. Previous research on mask detection using the Eigenface method yielded an accuracy of 88.89%, and another study using the Viola-Jones method yielded an accuracy of 95.5%. It can be concluded that this research can increase the accuracy value of previous studies. So, this research is feasible to be applied to the detection of mask use in real time.

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Classification SARS-CoV-2 Disease based on CT-Scan Image Using Convolutional Neural Network

Cited by 5 publications

References 27 publications

Alphabet Classification of Sign System Using Convolutional Neural Network with Contrast Limited Adaptive Histogram Equalization and Canny Edge Detection

Alphabet Classification of Sign System Using Convolutional Neural Network with Contrast Limited Adaptive Histogram Equalization and Canny Edge Detection

A Comparative Study of Javanese Script Classification with GoogleNet, DenseNet, ResNet, VGG16 and VGG19

Real-Time Detection of Face Mask Using Convolutional Neural Network

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