A Basic Concept of Image Classification for Covid-19 Patients Using Chest CT Scan and Convolutional Neural Network

Sari, Irma Permata; Widodo, Widodo; Nugraheni, Murien; Wanda, Putra

doi:10.1109/icitamee50454.2020.9398462

Cited by 7 publications

(3 citation statements)

References 19 publications

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“…They employed three learning rates: plateau, cyclic, and constant, however plateau produced the best results, with an accuracy of 0.90. Sari et al proposed [34] in 2020, using CNN with CT scan pictures to achieve a precision of 98.0 and an accuracy of 97.57. Tabik et al suggested [37] in 2020, using COVID-SDNET, which combines many approaches such as ResNet, U-Net, FuCiTNET [38], and CNN, to achieve an accuracy of 81.00 percent.…”

Section: Methods For Deep Learning Techniquementioning

confidence: 99%

Soft computing and image processing techniques for COVID-19 prediction in lung CT scan images

Appari¹,

Kanojia²

2022

HIS

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COVID-19 is a contagious respiratory illness that can be passed from person to person. Because it affects the lungs, damages blood arteries, and causes cardiac problems, COVID-19 must be diagnosed quickly. The reverse transcriptase polymerase chain reaction (RT-PCR) is a method for detecting COVID-19, but it is time consuming and labor expensive, as well as putting the person collecting the sample in danger. As a result, clinicians prefer to use CT scan and Xray images. COVID-19 classification can be done manually, however AI makes the process go faster. AI approaches include image processing, machine learning, and deep learning. An AI model is required to diagnose COVID-19, and a dataset is necessary to train that model. A dataset consists of the information from which the model is trained. This paper consists of the review of different image processing, machine learning and deep learning papers proposed by different researchers. As well as models based on deep learning and pretrained model using gradient boosting algorithm The goal of this paper is to provide information for future researchers to work with.

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Section: Methods For Deep Learning Techniquementioning

confidence: 99%

Soft computing and image processing techniques for COVID-19 prediction in lung CT scan images

Appari¹,

Kanojia²

2022

HIS

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“…The images from each class are given in Table I. On the other hand, verifying the model's generalization ability and the loss function value is done using a validation set [40].…”

Section: Datasetmentioning

confidence: 99%

“…We cut the whole dataset into a training set (18 classes), and a testing set (8 classes) or 70:30. Training the weight of the deep convolutional neural network is done by using the training set.On the other hand, verifying the model's generalization ability and the loss function value is done using a validation set[40].…”

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confidence: 99%

Closer Look at Image Classification for Indonesian Sign Language with Few-Shot Learning Using Matching Network Approach

Sari

2023

JOIV : Int. J. Inform. Visualization

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Huge datasets are important to build powerful pipelines and ground well to new images. In Computer Vision, the most basic problem is image classification. The classification of images may be a tedious job, especially when there are a lot of amounts. But CNN is known to be data-hungry while gathering. How can we build some models without much data? For example, in the case of Sign Language Recognition (SLR). One type of Sign Language Recognition system is vision-based. In Indonesian Sign Language dataset has a relatively small sample image. This research aims to classify sign language images using Computer Vision for Sign Language Recognition systems. We used a small dataset, Indonesian Sign Language. Our dataset is listed in 26 classes of alphabet, A-Z. It has loaded 12 images for each class. The methodology in this research is few-shot learning. Based on our experiment, the best accuracy for few-shot learning is Mnasnet1_0 (85.75%) convolutional network model for Matching Networks, and loss estimation is about 0,43. And the experiment indicates that the accuracy will be increased by increasing the number of shots. We can inform you that this model's matching network framework is unsuitable for the Inception V3 model because the kernel size cannot be greater than the actual input size. We can choose the best algorithm based on this research for the Indonesian Sign Language application we will develop further.

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Chest X-Ray-Based Covid-19 Detection Using Preprocessing Techniques and Deep Learning Algorithms

Kurian,

Thangam,

Raja

et al. 2024

Lecture Notes in Networks and Systems

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A Basic Concept of Image Classification for Covid-19 Patients Using Chest CT Scan and Convolutional Neural Network

Cited by 7 publications

References 19 publications

Soft computing and image processing techniques for COVID-19 prediction in lung CT scan images

Soft computing and image processing techniques for COVID-19 prediction in lung CT scan images

Closer Look at Image Classification for Indonesian Sign Language with Few-Shot Learning Using Matching Network Approach

Chest X-Ray-Based Covid-19 Detection Using Preprocessing Techniques and Deep Learning Algorithms

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