• Tidak ada hasil yang ditemukan

COVID-19 detection using deep learning classifiers and contrast-enhanced canny edge detected x-ray images

N/A
N/A
Info
Unduh - Bebas
Protected

Academic year: 2024

Membagikan "COVID-19 detection using deep learning classifiers and contrast-enhanced canny edge detected x-ray images"

Copied!
1
0
0

Memuat.... (Lihat teks lengkap sekarang)

Unduh sekarang ( 1 Halaman )

Teks penuh

(1)

COVID-19 detection using deep learning classifiers and contrast-enhanced canny edge detected x-ray images

ABSTRACT

COVID-19 is a deadly disease, and should be efficiently detected. COVID-19 shares similar symptoms with pneumonia, another type of lung disease, which remains a cause of morbidity and mortality. This article aims to demonstrate an ensemble deep learning

approach that can differentiate COVID-19 and pneumonia based on chest X-ray images. The original X-ray images were processed to produce two sets of images with different features.

The first set was images enhanced with contrast limited adaptive histogram equalization.

The second set was edge images produced by contrast-enhanced canny edge detection.

Convolutional neural networks were used to extract features from the images and train classifiers, which were able to classify COVID-19, pneumonia, and healthy lungs cases.

Results show that the classifiers were able to differentiate X-rays of different classes, where the best performing ensemble achieved an overall accuracy of 97.90%, with a sensitivity of 99.47%, and specificity of 98.94% for COVID-19 detection.

Referensi

Unduh sekarang ( PDF - 1 Halaman - 60.70 KB )

Dokumen terkait

PENINGKATAN KUALITAS CITRA X-RAY PARU-PARU PASIEN COVID-19 MENGGUNAKAN METODE CONTRAST LIMITED ADAPTIVE HISTOGRAM EQUALIZATION

Pada tahap ini dilakukan perancangan sistem yang akan dibangun, yaitu perancangan aplikasi peningkatan kualitas citra x-ray paru-paru pasien Covid-19 dengan metode

View of COVID-19 Severity based on Deep Convolutional Neural Networks Chest X-Ray Image in Aceh, Indonesia

This study aimed to create a deep learning model that can detect COVID-19 from a chest image dataset of confirmed patients treated at the provincial hospital in Aceh.. Methods:

IMPLEMENTASI DEEP LEARNING MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK UNTUK KLASIFIKASI CITRA X-RAY PENYAKIT COVID-19

Penelitian ini menggunakan metode Convolutional Neural Network (CNN) untuk mendeteksi citra x-ray penyakit Covid-19 dengan dataset yang digunakan sejumlah 450

Detection of COVID-19 Based on Chest X-rays Using Deep Learning

Two different DL approaches based on a pertained neural network model ResNet-50 for COVID-19 detection using chest X-ray CXR images are proposed in this study.. Augmenting, enhancing,

Detection of COVID-19 in Chest X-ray Images: A Big Data Enabled Deep Learning Approach

In this paper, the Apache Spark system has been utilized as an extensive data framework and applied a Deep Transfer Learning DTL method using Convolutional Neural Network CNN three

Detection of COVID-19 Based on Synthetic Chest X-Ray (CXR) Images Using Deep Convolutional Generative Adversarial Networks (DCGAN) and Transfer Learning

The training time for each detection model using the extended dataset with the addition of 4800 synthetic images 2400 Covid-19 and 2400 Normal The shorter training time of the

Covidense: Providing a Suitable Solution for Diagnosing Covid-19 Lung Infection Based on Deep Learning from Chest X-Ray Images of Patients

Thus, we propose Covidense based on the pre-trained Densenet-201 model and is trained on a dataset comprising chest X-ray images of Covid-19, normal, bacterial pneumonia, and viral

CLASSIFICATION OF COVID 19, PNEUMONIA AND NORMAL LUNGS BASED ON X-RAY IMAGES USING CONVOLUTIONAL NEURAL NETWORK

CONCLUSION From the results of research conducted to classify covid-19, pneumonia and normal lungs based on x-ray images using the Convolutional Neural Network CNN method, the