Detection Of CT - Scan Lungs COVID-19 Image Using Convolutional Neural Network And CLAHE
Ronaldus Morgan James,Andi Sunyoto +1 more
- pp 302-307
TLDR
In this article, the authors used three convolutional layers, three max-pooling layers, and two fully connected layers to detect COVID-19 lungs with 83.28% accuracy.Abstract:
Detecting COVID-19 is a significant task for medical professionals today because of its rapid spread. To overcome this problem, medical professionals have used various techniques and methods to detect to inhibit the proliferation of COVID-19. CT (Computed Tomography) Scan is currently the best method for detecting COVID-19. This diagnostic method is very accurate because it can see organs in three dimensions. However, this method requires a radiologist to detect the disease and requires a long time, which means it will cut valuable time for medical practitioners if a patient is sick. Therefore it is necessary to implement a system to detect the coronavirus automatically as an alternative quickly. This study intends to help medical practitioners to detect computed tomography (CT) Scans of lungs infected with COVID-19. The methods to be used are Limited Adaptive histogram equalization (CLAHE) contrast to improve the quality of CT (Computed Tomography) Scan images of COVID-19 lungs and Convolutional Neural Network (CNN) for the image classification process. The dataset used is 698 RGB images. This study uses three convolutional layers, 3 Max-Pooling layers, and two fully connected layers, resulting in 83.28% accuracy.read more
Citations
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CVD-HNet: Classifying Pneumonia and COVID-19 in Chest X-ray Images Using Deep Network
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Hybrid CLAHE-CNN Deep Neural Networks for Classifying Lung Diseases from X-ray Acquisitions
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TL;DR: A hybrid architecture of contrast-limited adaptive histogram equalization (CLAHE) and deep convolutional network for the classification of lung diseases and the experimental results indicate that the suggested hybrid architecture outperforms traditional methods by roughly 20% in terms of accuracy.
Proceedings ArticleDOI
Automated classification method of COVID-19 cases from chest CT volumes using 2D and 3D hybrid CNN for anisotropic volumes
Masahiro Okuda,Tong Zheng,T. Hayashi,Yoshito Otake,Masahiro Hashimoto,Toshiaki Akashi,Shigeki Aoki,Kensaku Mori +7 more
TL;DR: An automated classification method of chest CT volumes based on likelihood of COVID-19 cases that has a 2D/3D hybrid feature extraction flows that was evaluated was higher than that of a classification CNN which does not have 2D and 3D hybridfeature extraction flows.