Deteksi COVID-19 Berdasarkan Hasil Rontgen Dada (Chest Xray) Menggunakan Python

Pratama, Pebri Andhi Herry; Teguh, Rony; Sahay, Abertun Sagit; Wilentine, Valencia

doi:10.47111/jointecoms.v1i1.2956

Cited by 5 publications

(4 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A method is needed to read and diagnose the diseases afflicting patients. Chest x-ray image data, which is two-dimensional, can be processed using deep learning with the Convolutional Neural Network (CNN) method [3].…”

Section: Introductionmentioning

confidence: 99%

Implementation and Optimization Of Inception Resnet-v2 with Data Balancing (Case Study Of Lung Disease Classification)

Ashari Rakhmat,

Muhammad Yudantira

2024

E3S Web Conf.

View full text Add to dashboard Cite

Lungs are vital organs in humans because the process of breathing in humans occurs through the lung organs. However, there are diseases of the lungs, such as COVID-19, Pneumonia, and Tuberculosis that can disrupt the respiratory system in humans. Early detection is required by chest x-ray examination. The development of artificial intelligence technology can help classify chest x-rays with image analysis. In classification for image analysis, there are problems where the data is not balanced, which can cause errors in classification. Thus, data balancing is needed to balance the data. This study conducted training using InceptionResnet-v2 with data balancing. The best model performance results were obtained by training using random oversampling on the model using epoch 20, batch size 64, and learning rate 0.0001 with an accuracy value of 89.23%, loss 0.28, precision 90.05%, recall 89.88%, F1-score 89.74%, and AUC 98%. The accuracy value increased by 7.52% compared to the imbalanced dataset and by 1.29% compared to the random undersampling dataset. Abstract.

show abstract

Section: Introductionmentioning

confidence: 99%

Implementation and Optimization Of Inception Resnet-v2 with Data Balancing (Case Study Of Lung Disease Classification)

Ashari Rakhmat,

Muhammad Yudantira

2024

E3S Web Conf.

View full text Add to dashboard Cite

show abstract

“…The chest X-ray procedure provides an image based on the diffraction of electromagnetic waves, namely X-rays [1].With this light, the condition of the organs in the human body can be seen visually without surgery (non-invasive procedure). Of course, this examination must be carried out carefully, but basically, the reading of X-ray results tends to be done through visual observation, so an appropriate analysis is needed to distinguish the results of a chest X-ray from the condition of the organs [3].…”

Section: Introductionmentioning

confidence: 99%

Clustering of Tuberculosis and Normal Lungs Based on Image Segmentation Results of Chan-Vese and Canny with K-Means

Riyana Putri,

Wibowo,

Mustofa

2023

IJAIDM

View full text Add to dashboard Cite

The lungs are a vital organ in the human body. If there is interference with lung function, the health of the human body as a whole can be affected. Examination by medical workers needs to be done when there is interference with lung function. This examination can usually be done in various ways, one of which is through a chest X-ray radiographic examination procedure. The application of Artificial Intelligence is growing rapidly in the medical field, especially in diagnostics and treatment management. Artificial intelligence in the medical world can also be applied in processing image data in radiology to analyze X-ray results as supporting diagnostic information. Operators Chan-Vese and Canny are two edge detection operators in digital image processing in an effort to obtain the necessary information based on the shape and size of the object. This study was conducted for clustering of normal and tuberculosis lung conditions based on the results of chest X-ray image segmentation from Chan-Vese and Canny using K-Means Clustering. The results of clustering using K-Means obtained an accuracy value of 77.1%, a precision value of 88%, and a specificity value of 97.2%

show abstract

“…To confirm/diagnose the presence of pneumonia in a person's body, the doctor will perform a series of examinations, including chest radiographs, CT-Scans, and Magnetic Resonance Imaging (MRI) [4]. The examination method with chest radiograph is one of the most frequently used examinations in the form of medical images because it is more affordable [5]. The reading of the image from the chest radiograph has a drawback in the form of difficulty in detecting the disease so it takes a long time before medical personnel or doctors can diagnose the disease suffered by the patient.…”

Section: Introductionmentioning

confidence: 99%

Pneumonia Image Classification Using CNN with Max Pooling and Average Pooling

Nurjannah

Kurniasari

Sari

et al. 2022

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

View full text Add to dashboard Cite

Pneumonia is still a frequent cause of death in hundreds of thousands of children in most developing countries and generally detected clinically through chest radiographs. This method still difficult to detect the disease and requires a long time to produce a diagnosis. To simplify and shorten the detection process, we need a faster method and more precise in diagnosing pneumonia. This study aims to classify chest x-ray images using the CNN method to diagnose pneumonia. The proposed CNN model will be tested using max & average pooling. The proposed model is a development of the model in previous studies by adding batch normalization, dropout layer, and the number of epochs used. To maximize model performance, the dataset used will be optimized with oversampling & data augmentation techniques. The dataset used in this study is "Chest X-Ray Images (Pneumonia)" with a total of 5,856 data divided into two classes, namely Normal and Pneumonia. The proposed model gets 98% results using average pooling where the results increase by 9-13% better than the previous study. This is because overall pixel value of the image is highly considered to classify normal lungs and pneumonia.

show abstract

Deteksi COVID-19 Berdasarkan Hasil Rontgen Dada (Chest Xray) Menggunakan Python

Cited by 5 publications

References 1 publication

Implementation and Optimization Of Inception Resnet-v2 with Data Balancing (Case Study Of Lung Disease Classification)

Implementation and Optimization Of Inception Resnet-v2 with Data Balancing (Case Study Of Lung Disease Classification)

Clustering of Tuberculosis and Normal Lungs Based on Image Segmentation Results of Chan-Vese and Canny with K-Means

Pneumonia Image Classification Using CNN with Max Pooling and Average Pooling

Contact Info

Product

Resources

About