Ekstraksi Fitur Citra Radiografi Thorax Menggunakan DWT dan Moment Invariant

Wijaya, I Gede Pasek Suta; Avianty, Ditha Nurcahya; Bimantoro, Fitri; Lestari, Rina

doi:10.29303/jcosine.v5i2.423

Cited by 2 publications

(2 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At the same time, the android mobile application was evaluated by using the System Usability Scale (SUS). The accuracy, precision, and recall were determined based on the confusion matrix [25], [27], [28], commonly used to evaluate the CNN model as a classifier. The confusion matrix used in this research was 20x20 (see Table 4), consisting of the actual and predicted classes representing each disease class.…”

Section: A Testing Methodsmentioning

confidence: 99%

The Design of Convolutional Neural Networks Model for Classification of Ear Diseases on Android Mobile Devices

Wijaya

Mulyana

Kadriyan

et al. 2023

JOIV : Int. J. Inform. Visualization

View full text Add to dashboard Cite

An otorhinolaryngologist (ORL) or general practitioner diagnoses ear disease based on ear image information. However, general practitioners refer patients to ORL for chronic ear disease because the image of ear disease has high complexity, variety, and little difference between diseases. An artificial intelligence-based approach is needed to make it easier for doctors to diagnose ear diseases based on ear image information, such as the Convolutional Neural Network (CNN). This paper describes how CNN was designed to generate CNN models used to classify ear diseases. The model was developed using an ear image dataset from the practice of an ORL at the University of Mataram Teaching Hospital. This work aims to find the best CNN model for classifying ear diseases applicable to android mobile devices. Furthermore, the best CNN model is deployed for an Android-based application integrated with the Endoscope Ear Cleaning Tool Kit for registering patient ear images. The experimental results show 83% accuracy, 86% precision, 86% recall, and 4ms inference time. The application produces a System Usability Scale of 76.88% for testing, which shows it is easy to use. This achievement shows that the model can be developed and integrated into an ENT expert system. In the future, the ENT expert system can be operated by workers in community health centres/clinics to assist leading health them in diagnosing ENT diseases early.

show abstract

Section: A Testing Methodsmentioning

confidence: 99%

The Design of Convolutional Neural Networks Model for Classification of Ear Diseases on Android Mobile Devices

Wijaya

Mulyana

Kadriyan

et al. 2023

JOIV : Int. J. Inform. Visualization

View full text Add to dashboard Cite

show abstract

“…The comparison is made because the two calculation methods have similarities in the information to be considered, distinguishing them in the settlement process. Additionally, this study is a development of the DWT and Moment Invariant-based features of chest x-ray images [18] and Covid-19 prediction based on DWT and moment invariant features and ANN classifier [19]. The main aim of this work is to find the best SVM model appropriate for mentioned features.…”

Section: Introductionmentioning

confidence: 99%

The Comparison of SVM and ANN Classifier for COVID-19 Prediction

Avianty¹,

Wijaya

Bimantoro³

2022

LKJITI

Self Cite

View full text Add to dashboard Cite

Coronavirus 2 (SARS-CoV-2) is the cause of an acute respiratory infectious disease that can cause death, popularly known as Covid-19. Several methods have been used to detect COVID-19-positive patients, such as rapid antigen and PCR. Another method as an alternative to confirming a positive patient for COVID-19 is through a lung examination using a chest X-ray image. Our previous research used the ANN method to distinguish COVID-19 suspect, pneumonia, or expected by using a Haar filter on Discrete Wavelet Transform (DWT) combined with seven Hu Moment Invariants. This work adopted the ANN method's feature sets for the Support Vector Machine (SVM), which aim to find the best SVM model appropriate for DWT and Hu moment-based features. Both approaches demonstrate promising results, but the SVM approach has slightly better results. The SVM's performances improve accuracy to 87.84% compared to the ANN approach with 86% accuracy.

show abstract

Ekstraksi Fitur Citra Radiografi Thorax Menggunakan DWT dan Moment Invariant

Cited by 2 publications

References 3 publications

The Design of Convolutional Neural Networks Model for Classification of Ear Diseases on Android Mobile Devices

The Design of Convolutional Neural Networks Model for Classification of Ear Diseases on Android Mobile Devices

The Comparison of SVM and ANN Classifier for COVID-19 Prediction

Contact Info

Product

Resources

About