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15 results on '"Chawki, Youness"'

1. Automated Methods for Detection and Classification Pneumonia based on X-Ray Images Using Deep Learning

Author

Asnaoui, Khalid El, Chawki, Youness, and Idri, Ali

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Recently, researchers, specialists, and companies around the world are rolling out deep learning and image processing-based systems that can fastly process hundreds of X-Ray and computed tomography (CT) images to accelerate the diagnosis of pneumonia such as SARS, COVID-19, and aid in its containment. Medical images analysis is one of the most promising research areas, it provides facilities for diagnosis and making decisions of a number of diseases such as MERS, COVID-19. In this paper, we present a comparison of recent Deep Convolutional Neural Network (DCNN) architectures for automatic binary classification of pneumonia images based fined tuned versions of (VGG16, VGG19, DenseNet201, Inception_ResNet_V2, Inception_V3, Resnet50, MobileNet_V2 and Xception). The proposed work has been tested using chest X-Ray & CT dataset which contains 5856 images (4273 pneumonia and 1583 normal). As result we can conclude that fine-tuned version of Resnet50, MobileNet_V2 and Inception_Resnet_V2 show highly satisfactory performance with rate of increase in training and validation accuracy (more than 96% of accuracy). Unlike CNN, Xception, VGG16, VGG19, Inception_V3 and DenseNet201 display low performance (more than 84% accuracy).

Published
2020

3. Towards a Rich and Dynamic Human Digital Memory in Egocentric Dataset

Author

El Ansaoui, Khalid, Chawki, Youness, Ouhda, Mohammed, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, and Farhaoui, Yousef, editor

Published
2020
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5. Classification and detection of Covid-19 based on X-Ray and CT images using deep learning and machine learning techniques: A bibliometric analysis.

Author

Chawki, Youness, Elasnaoui, Khalid, and Ouhda, Mohamed

Subjects
MACHINE learning, DEEP learning, BIBLIOMETRICS, X-ray imaging, COMPUTED tomography, COVID-19 pandemic, X-rays
Abstract

During the COVID-19 pandemic, it was crucial for the healthcare sector to detect and classify the virus using X-ray and CT scans. This has underlined the need for advanced Deep Learning and Machine Learning approaches to effectively spot and manage the virus's spread. Indeed, researchers worldwide have dynamically participated in the field by publishing an important number of papers across various databases. In this context, we present a bibliometric analysis focused on the detection and classification of COVID-19 using Deep Learning and Machine Learning techniques, based on X-Ray and CT images. We analyzed published documents of the six prominent databases (IEEE Xplore, ACM, MDPI, PubMed, Springer, and ScienceDirect) during the period between 2019 and November 2023. Our results showed that rising forces in economy and technology, especially India, China, Turkey, and Pakistan, began to compete with the great powers in the field of scientific research, which could be seen from their number of publications. Moreover, researchers contributed to Deep Learning techniques more than the use of Machine Learning techniques or the use of both together and preferred to submit their works to Springer Database. An important result was that more than 57% documents were published as Journal Articles, which was an important portion compared to other publication types (conference papers and book chapters). Moreover, the PubMed journal Multimedia Tools and Applications' tops the list of journals with a total of 29 published articles. [ABSTRACT FROM AUTHOR]

Published
2024
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12. A fully automatic system for assessing day similarity.

Author

CHAWKI, Youness, EL ASNAOUI, Khalid, and OUHDA, Mohammed

Subjects
MULTIMEDIA systems, DIGITAL libraries, ALGORITHMS, AUTOMATION, DATA mining
Abstract

Due to the explosion of multimedia quantities such as archived TV broad cast videos, various multimedia resources released on the Internet, intense work in multimedia retrieval domain has aimed to provide efficient and accurate functionality for users to access the desired information. The rapid access to these huge multimedia resources and automatically assess day similarity or an experience from this collection of unstructured egocentric data presents major challenges that require efficient algorithms. As everything is nowadays automated, the main goal of this work is to apply a new method to automatically assess day similarity using a real image dataset called Egocentric Dataset of the University of Barcelona (EDUB) of 4912 daily images acquired by 4 persons in order to evaluate the performance of our system. [ABSTRACT FROM AUTHOR]

Published
2020

13. CBIR using the 2-D ESPRIT method: Application to Coil_100 database

Author

El Asnaoui Khalid, Ouanan Mohammed, Aksasse Brahim, and Chawki Youness

Subjects
Database, Computer science, business.industry, Feature extraction, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Pattern recognition, Image processing, Content-based image retrieval, computer.software_genre, Automatic image annotation, Image texture, Computer vision, Visual Word, Artificial intelligence, business, computer, Image retrieval, Feature detection (computer vision)
Abstract

Most retrieval systems are mostly based on key words for image search, but in many case it cannot meet demands for different user with different view. In this paper, a new approach of content based image retrieval (CBIR) is presented, which is based on the image frequency content. Indeed, we have used the 2-D ESPRIT (Estimation of Signal Parameters via Rotationnal Invariance Techniques) method to extract from the image the frequency content for constructing the vector descriptor. Our approach is applied to the Coil_100 database and the experimental results show that this method improves the precision of image retrieval.

Published
2015

14. Content frequency and shape features based on CBIR: application to colour images

Author

Ouanan Mohammed, Chawki Youness, El Asnaoui Khalid, and Aksasse Brahim

Subjects
0301 basic medicine, 030103 biophysics, Control and Optimization, Estimation of signal parameters via rotational invariance techniques, Angular radial transform, business.industry, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, General Engineering, Pattern recognition, Image (mathematics), 03 medical and health sciences, Robustness (computer science), Electromagnetic coil, Content (measure theory), Discrete Mathematics and Combinatorics, Spectral analysis, Artificial intelligence, business, Image retrieval, Mathematics
Abstract

Due to the diversity of the image content, we propose in this study a new technique for content-based image retrieval to characterise the image. In this scenario, all images are characterised by their frequency content and their shape information. Indeed, using the high-resolution spectral analysis methods, especially the 2-D estimation of signal parameters via rotational invariance techniques, we extract from the image its content frequency and with the angular radial transform its shape information to construct a new vector descriptor. The experimental results applied to the Coil_100 database show the effectiveness and the robustness of our approach compared with the method based on the shame information only, and the precision average can be reached as 74.49%.

Published
2018

15. New Method of Content Based Image Retrieval based on 2-D ESPRIT Method and the Gabor Filters

Author

Chawki Youness, El Asnaoui Khalid, Ouanan Mohammed, and Aksasse Brahim

Subjects
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Abstract

We propose, in this paper, a new method for Content Based Image Retrieval (CBIR) by exploiting the digital image content. Our method is based on the representation of the digital image content by a characteristics vector of the indexed image. Indeed, we have exploited the image texture to extract its characteristics and for constructing a new descriptor vector by combining the Bidimensional High Resolution Spectral Analysis 2-D ESPRIT (Estimation of Signal Parameters via Rotationnal Invariance Techniques) method and Gabor filter. To evaluate the performance, we have tested our approach on Brodatz image database. The results show that the representation of the digital image content appears significant in research of imaging information.

Published
2015

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