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21 results on '"HABCHI, Yassine"'

1. Deep Transfer Learning for Kidney Cancer Diagnosis

Author

Habchi, Yassine, Kheddar, Hamza, Himeur, Yassine, Boukabou, Abdelkrim, Atalla, Shadi, Mansoor, Wathiq, and Al-Ahmad, Hussain

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

Many incurable diseases prevalent across global societies stem from various influences, including lifestyle choices, economic conditions, social factors, and genetics. Research predominantly focuses on these diseases due to their widespread nature, aiming to decrease mortality, enhance treatment options, and improve healthcare standards. Among these, kidney disease stands out as a particularly severe condition affecting men and women worldwide. Nonetheless, there is a pressing need for continued research into innovative, early diagnostic methods to develop more effective treatments for such diseases. Recently, automatic diagnosis of Kidney Cancer has become an important challenge especially when using deep learning (DL) due to the importance of training medical datasets, which in most cases are difficult and expensive to obtain. Furthermore, in most cases, algorithms require data from the same domain and a powerful computer with efficient storage capacity. To overcome this issue, a new type of learning known as transfer learning (TL) has been proposed that can produce impressive results based on other different pre-trained data. This paper presents, to the best of the authors' knowledge, the first comprehensive survey of DL-based TL frameworks for kidney cancer diagnosis. This is a strong contribution to help researchers understand the current challenges and perspectives of this topic. Hence, the main limitations and advantages of each framework are identified and detailed critical analyses are provided. Looking ahead, the article identifies promising directions for future research. Moving on, the discussion is concluded by reflecting on the pivotal role of TL in the development of precision medicine and its effects on clinical practice and research in oncology., Comment: 32 pages, 8 figures and 8 tables

Published
2024

2. Machine Learning and Vision Transformers for Thyroid Carcinoma Diagnosis: A review

Author

Habchi, Yassine, Kheddar, Hamza, Himeur, Yassine, Boukabou, Abdelkrim, Chouchane, Ammar, Ouamane, Abdelmalik, Atalla, Shadi, and Mansoor, Wathiq

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

The growing interest in developing smart diagnostic systems to help medical experts process extensive data for treating incurable diseases has been notable. In particular, the challenge of identifying thyroid cancer (TC) has seen progress with the use of machine learning (ML) and big data analysis, incorporating transformers to evaluate TC prognosis and determine the risk of malignancy in individuals. This review article presents a summary of various studies on AIbased approaches, especially those employing transformers, for diagnosing TC. It introduces a new categorization system for these methods based on artifcial intelligence (AI) algorithms, the goals of the framework, and the computing environments used. Additionally, it scrutinizes and contrasts the available TC datasets by their features. The paper highlights the importance of AI instruments in aiding the diagnosis and treatment of TC through supervised, unsupervised, or mixed approaches, with a special focus on the ongoing importance of transformers in medical diagnostics and disease management. It further discusses the progress made and the continuing obstacles in this area. Lastly, it explores future directions and focuses within this research feld.

Published
2024

3. AI in Thyroid Cancer Diagnosis: Techniques, Trends, and Future Directions

Author

Habchi, Yassine, Himeur, Yassine, Kheddar, Hamza, Boukabou, Abdelkrim, Atalla, Shadi, Chouchane, Ammar, Ouamane, Abdelmalik, and Mansoor, Wathiq

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Artificial Intelligence, Computer Science - Computers and Society, Computer Science - Machine Learning
Abstract

There has been a growing interest in creating intelligent diagnostic systems to assist medical professionals in analyzing and processing big data for the treatment of incurable diseases. One of the key challenges in this field is detecting thyroid cancer, where advancements have been made using machine learning (ML) and big data analytics to evaluate thyroid cancer prognosis and determine a patient's risk of malignancy. This review paper summarizes a large collection of articles related to artificial intelligence (AI)-based techniques used in the diagnosis of thyroid cancer. Accordingly, a new classification was introduced to classify these techniques based on the AI algorithms used, the purpose of the framework, and the computing platforms used. Additionally, this study compares existing thyroid cancer datasets based on their features. The focus of this study is on how AI-based tools can support the diagnosis and treatment of thyroid cancer, through supervised, unsupervised, or hybrid techniques. It also highlights the progress made and the unresolved challenges in this field. Finally, the future trends and areas of focus in this field are discussed., Comment: 30 pages, 16 figures and 10 tables

Published
2023

5. Image Classification of Leukemia Cancer Using Wavelet Deep Neural Network.

Author

HABCHI, Yassine, BOUDDOU, Riyadh, and AIMER, Ameur Fethi

Subjects
ARTIFICIAL neural networks, IMAGE recognition (Computer vision), CONVOLUTIONAL neural networks, TUMOR classification, IMAGE processing, IMAGE compression
Abstract

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Published
2024
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10. MRI image compression using level set method and biorthogonal CDF wavelet based on lifting scheme

Author

Habchi yassine, Imane Haouam, Ridha Ilyas Bendjillali, and Mohammed Beladgham

Subjects
Set partitioning in hierarchical trees, Active contour model, Level set method, Level set, Wavelet, Lifting scheme, Computer science, Biorthogonal system, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Algorithm, Image compression
Abstract

image compression optimizes the performance of any digital system by reducing time and cost. interested parties In medical diagnostics field provide more information about the image with a precision and completeness of diagnosis which related to a good quality, so the main objective of compression is to research an optimal reduction of image size without losing the quality. In this article, we proposed a medical image compression algorithm that combines geometric active contour model and biorthogonal wavelet transform. In this method it is necessary to localize the region of interest, using the level set for an optimal reduction, then we use the lifting scheme biorthogonal CDF (biorthogonal lifting scheme CDF9/7, Gall 5/3 and FB), coupled with the set partitioning in hierarchical trees algorithm., the proposed algorithm is superior to traditional methods for MRI images. The level set and CDF9/7 LIFTING scheme algorithm coupled with SPIHT provides very important PSNR (Peak Signal to Noise Ration) and MSSIM (Mean Structural Similarity) values

Published
2018

12. New Contribution on Compression Color Images: Analysis and Synthesis for Telemedicine Applications

Author

Taleb Ahmed Abdelmalik, Bassou Abdesselam, Habchi Yassine, Moulay Lakhdar Abdelmouneim, and Beladgham Mohammed

Subjects
Signal processing, business.industry, Computer science, Color image, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Wavelet transform, Wavelet, Multiple time, Computer vision, Artificial intelligence, business, Algorithm, Image compression, Coding (social sciences), Data compression
Abstract

The wavelets are a recent tool for signal processing analysis, for multiple time scale. It gives rise to many applications in various fields such as geophysics, astrophysics, telecommunications, imaging, and video coding. They are the basis of new analytical techniques and signal synthesis and some nice applications for general problems such as compression. This paper introduces an application for color medical image compression based on the wavelet transform coupled with SPI HT coding algorithm. In order to enhance the compression by this algorithm, we have compared the results obtained with wavelet transform application in natural, medical and satellite color image field. For this reason, we evaluated two parameters known for their calculation speed. The first parameter is the PSNR; the second is MSSIM (structural similarity) j m n j l k f A m l h n k h f A 1 , , 2 2

Published
2014

13. The Performance of Discret Bandelet Transform Coupled by SPIHT Coder to Improve the Visuel Quality of Biomedical Color Image Compression

Author

Abdesselam Bassou, Taleb Ahmed Abdelmalik, Habchi Yassine, Moulay Lakhdar Abdelmouneim, and Beladgham Mohammed

Subjects
Bandelet, Image quality, business.industry, Binary image, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Top-hat transform, Image processing, Image (mathematics), Set partitioning in hierarchical trees, Computer Science::Computer Vision and Pattern Recognition, Computer vision, Artificial intelligence, business, Image compression, Mathematics
Abstract

The search for a good representation is a central problem of image processing, this paper explores a new transform type to solve this problem. Color Image compression is now essential for applications such as transmission and storage in data. In the field of medical diagnostics, interested parties have resorted increasingly to color medical imaging. It is well established that the accuracy and completeness of diagnosis are initially connected with the image quality. This paper introduces an algorithm for color medical image compression based on the bandelet transform coupled with SPI HT coding algorithm;bandelet transform is a new method based on capturing the complex geometric content in image. The goal of this paper is to examine the capacity of this transform proposed to offer an optimal representation for image geometric, In order to enhance the compression by our algorithm, we have compared the results obtained with bandelet transform application in satellite image field. For this reason, we evaluated two parameters known for their calculation speed. The first parameter is the PSNR; the second is MSSIM (structural similarity) to measure the quality of compressed image. We concluded that the results obtained are very satisfactory for color medical image domain

Published
2014

14. Performance Evaluation of DWT Compared to DCT for Compression Biomedical Image

Author

Habchi Yassine, Beladgham Mohammed, Bassou Abdesselam, Moulay Lakhdar Abdelmouneim, and Taleb Ahmed Abdelmalik

Subjects
Pixel, business.industry, Computer science, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Data_CODINGANDINFORMATIONTHEORY, Luminance, Computer Science Applications, Education, Wavelet, Discrete cosine transform, Computer vision, Artificial intelligence, business, Quantization (image processing), Encoder, Coding (social sciences), Image compression
Abstract

The image compression has for objective to reduce the volume of data required by the encoding of image, for applications of transmission or saving. For this we use the redundancies which exists within an image (a pixel has a good chance of having a luminance close to those of its neighbors) or between successive images in a sequence. We limit ourselves to the exploitation of redundancies within an image and we will work on gray level images of size 512x512. For image coding we chose an encoder based on progressive coding of data, coder is EZW (EMBEDDED WAVELET ZeroTree ENCODING, Shapiro 1993), the basis of this encoder a comparison is made between two types of transforms DWT (DISCREET WAVELETS TRANSFORM) and DCT (DISCRETE COSINE TRANSFORM) just to have the type of transformation that allows us to have a better visual quality of the image after decomposition. Visual quality image is judged by two important devaluation parameters PSNR and MSSIM.

Published
2014

15. A Comparative Study between X_Lets Family for Image Denoising

Author

Abdesselam Bassou, Beladgham Mohamed, Habchi Yassine, Moulay Lakhdar Abdelmouneim, and Taleb Ahmed Abdelmalik

Subjects
Discrete wavelet transform, Bandelet, business.industry, Stationary wavelet transform, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Wavelet transform, Top-hat transform, Hough transform, law.invention, law, Computer vision, Artificial intelligence, business, Harmonic wavelet transform, S transform, Mathematics
Abstract

Research good representation is a problem in image processing for this, our works are focused in developing and proposes some new transform which can represent the edge of image more efficiently, Among these transform we find the wavelet and ridgelet transform these both types transforms are not optimal for images with complex geometry, so we replace this two types classical transform with other effectiveness transform named bandelet transform, this transform is appropriate for the analysis of edges of the images and can preserve the detail information of high frequency of noisy image. De-noising is one of the most interesting and widely investigated topics in image processing area. In order to eliminate noise we exploit in this paper the geometrical advantages offered by the bandelet transform to solve the problem of image de-noising. To arrive to determine which type transform allows us high quality visual image, a comparison is made between bandelet, curvelet, ridgelet and wavelet transform, after determining the best transform, we going to determine which type of image is adapted to this transform. Numerically, we show that bandelet transform can significantly outperform and gives good performances for medical image type TOREX, and this is justified by a higher PSNR value for gray images.

Published
2014

16. A Comparative Study between Bandelet and Wavelet Transform Coupled by EZW and SPIHT Coder for Image Compression

Author

Taleb Ahmed Abdelmalik, Habchi Yassine, Moulay Lakhdar Abdelmouneim, and Beladgham Mohammed

Subjects
business.industry, Bandelet, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Wavelet transform, Field (computer science), Image (mathematics), Set partitioning in hierarchical trees, Metric (mathematics), Computer vision, Artificial intelligence, business, Representation (mathematics), Mathematics, Image compression
Abstract

Second generation bandelet transform is a new method based on capturing the complex geo metric content in image; we use this transform to study medical and satellite image co mpressed using the bandelet transform coupled by SPIHT coder. The goal of this paper is to examine the capacity of this transform proposed to offer an optimal representation for image geometric. We are interested in co mpressed medical image, In order to develop the compressed algorithm we compared our results with those obtained by the bandelet transform application in satellite image field. We concluded that the results obtained are very satisfactory for med ical image do main.

Published
2013

18. Hybrid DCT-DBT Algorithm for Medical Video Compression.

Author

HABCHI, Yassine, BELADGHAM, Mohammed, BENLABBES, Haouari, and ABDELMALIK, Ahmed Taleb

Subjects
VIDEO compression, DISCRETE cosine transforms, VIDEO coding
Abstract

In this paper we present the effect of transform type in medical video compression. To obtain the maximal capabilities of storage and transmission, different compression algorithms should be compared to find the optimal technique for medical video compression. We use for compression three type of transforms (DCT, DBT and DCT-DBT) where it combined with SPIHT coder separately. The DCT transform reduces the inter-frame redundancy and the DBT transform reduces the intra-frame redundancy. The objective of this study is to provide the robustness of compression at low bitrate, especially when the improvement in video compression filed is mainly related to the need of rapid and efficient techniques for the storage and transmission of data, where its taking on an increasingly critical role in healthcare. Simulation results show that the proposed hybrid algorithm (DCT-DBT+SPIHT) is outperforms than DBT+SPIHT by 0.5844%, 4.4694% and 0.1986% for MRI, MR ANGIOGRAPHY and HEART separately at low bitrate in medical domain. [ABSTRACT FROM AUTHOR]

Published
2016

19. The Effect of Error Transmission on Compressed Image using Vector Quantization with Different Codebooks.

Author

ELAWADY, Iman, MOULAY LAKHDAR, Abdelmounaim, BELADGHAM, Mohammed, HABCHI, Yassine, and BASSOU, Abdesselam

Subjects
VECTOR quantization, IMAGE compression, SIGNAL filtering
Abstract

In this paper we present the effect of error transmission on compressed image. We use the VQ algorithm for compression with two different generation method of codebook. The first is based on the LBG algorithm, while the second uses self-organizing maps (SOM).The objective of this study is to cheek the robustness of transmission with different BER (10-1 ,10-2 ,10-3) .the comparative study takes in consideration the quality of received image, filtered and recovered finally the combination between recovering and filtering. This paper is organized as follows: Section 2 presents vector quantization as technique of compression, Then, Section 3 the generation of codebook with SOM and LBG, After that in Section 4 the transmission channel, and in Section 5 the indices recovery, Finally, in Section 6, simulation results and discussion in section 7. [ABSTRACT FROM AUTHOR]

Published
2016

20. Low Bit-Rate Medical Video Coding Using DWT.

Author

HABCHI, Yassine, BELADGHAM, Mohamed, MOULAY LAKHDAR, Abdelmouneim, BASSOU, Abdesselam, and ABDELMALIK, Taleb-Ahmed

Subjects
ALGORITHMS, FRAMES (Video), DISCRETE wavelet transforms, DATA compression, VIDEO coding, MOTION estimation (Signal processing)
Abstract

In this age of multimedia, finding effective ways to store and transmit large volume data compression such as video remains a hot researching area for explored to investigate especially to the diagnostic medicals domains. In this paper, we propose an algorithm to compress or to reduce the redundancy found in different frames of sequence video. For this reason, we must be able to find effective coder who permits us to obtain a high quality visual of a sets frames. Our algorithm is based on DWT (for different filters) coupled with EZW coder. According to our experiments, the proposed algorithm offers to us a high value (28.3281dB) at 5Mbps. Comparing to other sequences video, the proposing method is very performs better for MRI sequence. Consequently, our method successfully is very suitable to medical sequences video. [ABSTRACT FROM AUTHOR]

Published
2015

21. A Novel Method to improve the Visual Quality of Biomedical Image Compression using Discret Bandelet Transform based on Lifting Scheme and SPIHT Coder.

Author

BELADGHAM, Mohammed, HABCHI, Yassine, LAKHDAR, Abdelmouneim Moulay, and BASSOU, Abdesselam

Subjects
OPTICAL flow, OPTICS, QUADTREES, MAGNETIC flux compression, ALGORITHMS
Abstract

Copyright of Electrotehnica, Electronica, Automatica is the property of ICPE SA (Editura Electra) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Published
2014

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