4 research outputs found
Improving performance of CNN to predict likelihood of COVID-19 using chest X-ray images with preprocessing algorithms
As the rapid spread of coronavirus disease (COVID-19) worldwide, chest X-ray
radiography has also been used to detect COVID-19 infected pneumonia and assess
its severity or monitor its prognosis in the hospitals due to its low cost, low
radiation dose, and wide accessibility. However, how to more accurately and
efficiently detect COVID-19 infected pneumonia and distinguish it from other
community-acquired pneumonia remains a challenge. In order to address this
challenge, we in this study develop and test a new computer-aided diagnosis
(CAD) scheme. It includes several image pre-processing algorithms to remove
diaphragms, normalize image contrast-to-noise ratio, and generate three input
images, then links to a transfer learning based convolutional neural network (a
VGG16 based CNN model) to classify chest X-ray images into three classes of
COVID-19 infected pneumonia, other community-acquired pneumonia and normal
(non-pneumonia) cases. To this purpose, a publicly available dataset of 8,474
chest X-ray images is used, which includes 415 confirmed COVID-19 infected
pneumonia, 5,179 community-acquired pneumonia, and 2,880 non-pneumonia cases.
The dataset is divided into two subsets with 90% and 10% of images in each
subset to train and test the CNN-based CAD scheme. The testing results achieve
94.0% of overall accuracy in classifying three classes and 98.6% accuracy in
detecting Covid-19 infected cases. Thus, the study demonstrates the feasibility
of developing a CAD scheme of chest X-ray images and providing radiologists
useful decision-making supporting tools in detecting and diagnosis of COVID-19
infected pneumonia.Comment: 11 pages, 5 figures, 2 table
An approach to human iris recognition using quantitative analysis of image features and machine learning
The Iris pattern is a unique biological feature for each individual, making
it a valuable and powerful tool for human identification. In this paper, an
efficient framework for iris recognition is proposed in four steps. (1) Iris
segmentation (using a relative total variation combined with Coarse Iris
Localization), (2) feature extraction (using Shape&density, FFT, GLCM, GLDM,
and Wavelet), (3) feature reduction (employing Kernel-PCA) and (4)
classification (applying multi-layer neural network) to classify 2000 iris
images of CASIA-Iris-Interval dataset obtained from 200 volunteers. The results
confirm that the proposed scheme can provide a reliable prediction with an
accuracy of up to 99.64%
Deep learning denoising for EOG artifacts removal from EEG signals
There are many sources of interference encountered in the
electroencephalogram (EEG) recordings, specifically ocular, muscular, and
cardiac artifacts. Rejection of EEG artifacts is an essential process in EEG
analysis since such artifacts cause many problems in EEG signals analysis. One
of the most challenging issues in EEG denoising processes is removing the
ocular artifacts where Electrooculographic (EOG), and EEG signals have an
overlap in both frequency and time domains. In this paper, we build and train a
deep learning model to deal with this challenge and remove the ocular artifacts
effectively. In the proposed scheme, we convert each EEG signal to an image to
be fed to a U-NET model, which is a deep learning model usually used in image
segmentation tasks. We proposed three different schemes and made our U-NET
based models learn to purify contaminated EEG signals similar to the process
used in the image segmentation process. The results confirm that one of our
schemes can achieve a reliable and promising accuracy to reduce the Mean square
error between the target signal (Pure EEGs) and the predicted signal (Purified
EEGs)
Image quality enhancement in wireless capsule endoscopy with adaptive fraction gamma transformation and unsharp masking filter
Wireless Capsule Endoscopy (WCE) presented in 2001 as one of the key
approaches to observe the entire gastrointestinal (GI) tract, generally the
small bowels. It has been used to detect diseases in the gastrointestinal
tract. Endoscopic image analysis is still a required field with many open
problems. The quality of many images it produced is rather unacceptable due to
the nature of this imaging system, which causes some issues to prognosticate by
physicians and computer-aided diagnosis. In this paper, a novel technique is
proposed to improve the quality of images captured by the WCE. More
specifically, it enhanced the brightness, contrast, and preserve the color
information while reducing its computational complexity. Furthermore, the
experimental results of PSNR and SSIM confirm that the error rate in this
method is near to the ground and negligible. Moreover, the proposed method
improves intensity restricted average local entropy (IRMLE) by 22%, color
enhancement factor (CEF) by 10%, and can keep the lightness of image
effectively. The performances of our method have better visual quality and
objective assessments in compare to the state-of-art methods.Comment: 7 pages, 7 figure