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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Indonesian Journal of Electrical Engineering and Computer Science
Mustapha Khelifi
Tahri Mohammed University
Computer Science & IT

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Data analysis for image transmitted using Discrete Wavelet Transform and Vector Quantization compression Mustapha Khelifi; Abdelmounaim Moulay Lakhdar; Iman Elawady
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 14, No 2: June 2016
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v14i2.2947

Abstract

In this paper we are going to study the effect of channel noise in image compressed with vector quantization and discrete wavelet transform. The objective of this study is to analyze and understand the way that the noise attack transmitted data by doing lot of tests like dividing the indices in different levels according to discrete wavelet transform and dividing  each level in frames of bits. The collected information well helps us to propose solutions to make the received image more resistible to the channel noise also to benefit from the good representation obtained by using vector quantization and discrete wavelet transform.
Interleaved reception method for restored vector quantization image Iman Elawady; Abdelmounaim Moulay Lakhdar; Mustapha Khelifi
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 14, No 4: December 2016
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v14i4.3181

Abstract

The transmission of image compression by vector quantization produce wrong blocks in received image which are completely different to the original one that makes the restoration process too hard because we don’t have any information about the original blocks. As a solution of this problem we try to keep the maximum of pixels that form the original block by building new blocks. Our proposition is based on decomposition and interleaving. For the simulation we use a binary symmetric channel with different BER and in the restoration process we use simple median filter just to check the efficiency of proposed approach.
Pneumonia detection based on transfer learning and a combination of VGG19 and a CNN Built from scratch Oussama Dahmane; Mustapha Khelifi; Mohammed Beladgham; Ibrahim Kadri
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 3: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i3.pp1469-1480

Abstract

In this paper, to categorize and detect pneumonia from a collection of chest X-ray picture samples, we propose a deep learning technique based on object detection, convolutional neural networks, and transfer learning. The proposed model is a combination of the pre-trained model (VGG19) and our designed architecture. The Guangzhou Women and Children's Medical Center in Guangzhou, China provided the chest X-ray dataset used in this study. There are 5,000 samples in the data set, with 1,583 healthy samples and 4,273 pneumonia samples. Preprocessing techniques such as contrast limited adaptive histogram equalization (CLAHE) and brightness preserving bi-histogram equalization was also used (BBHE) to improve accuracy. Due to the imbalance of the data set, we adopted some training techniques to improve the learning process of the samples. This network achieved over 99% accuracy due to the proposed architecture that is based on a combination of two models. The pre-trained VGG19 as feature extractor and our designed convolutional neural network (CNN).
Co-Authors Abdelmounaim Moulay Lakhdar Ibrahim Kadri Iman Elawady Mohammed Beladgham Oussama Dahmane