Proposed Face Detection Classification Model Based on Amazon Web Services Cloud (AWS)

Joodi, Mohanad Azeez; Saleh, Muna Hadi; Khadhim, Dheya Jassim

doi:10.31026/j.eng.2023.04.12

Cited by 4 publications

(2 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Generally, there has been a lot of focus on computation cost [ 30 , 31 ]. It is considered as a key element which assist in ill-conditioning.…”

Section: Introductionmentioning

confidence: 99%

Performance enhancement of high order Hahn polynomials using multithreading

Mahmmod,

Flayyih,

Fakhri

et al. 2023

PLoS ONE

View full text Add to dashboard Cite

Orthogonal polynomials and their moments have significant role in image processing and computer vision field. One of the polynomials is discrete Hahn polynomials (DHaPs), which are used for compression, and feature extraction. However, when the moment order becomes high, they suffer from numerical instability. This paper proposes a fast approach for computing the high orders DHaPs. This work takes advantage of the multithread for the calculation of Hahn polynomials coefficients. To take advantage of the available processing capabilities, independent calculations are divided among threads. The research provides a distribution method to achieve a more balanced processing burden among the threads. The proposed methods are tested for various values of DHaPs parameters, sizes, and different values of threads. In comparison to the unthreaded situation, the results demonstrate an improvement in the processing time which increases as the polynomial size increases, reaching its maximum of 5.8 in the case of polynomial size and order of 8000 × 8000 (matrix size). Furthermore, the trend of continuously raising the number of threads to enhance performance is inconsistent and becomes invalid at some point when the performance improvement falls below the maximum. The number of threads that achieve the highest improvement differs according to the size, being in the range of 8 to 16 threads in 1000 × 1000 matrix size, whereas at 8000 × 8000 case it ranges from 32 to 160 threads.

show abstract

“…Generally, there has been a lot of focus on computation cost [ 30 , 31 ]. It is considered as a key element which assist in ill-conditioning.…”

Section: Introductionmentioning

confidence: 99%

Performance enhancement of high order Hahn polynomials using multithreading

Mahmmod,

Flayyih,

Fakhri

et al. 2023

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…The primary objective is to aid doctors in accurately diagnosing brain diseases by eliminating non-brain tissues and preserving the brain (Tariq et al, 2017). This system aims to enhance the ease, speed, and accuracy of diagnosis and treatment, offering a potential solution by using the segmentation model to address the challenges posed by manual skull stripping which is achieved by employing CNN pre-trained models that have different versions used in various fields (Joodi et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Skull Stripping Based on the Segmentation Models

Nayyef,

Al-Tammi

2023

jcoeng

View full text Add to dashboard Cite

Skull image separation is one of the initial procedures used to detect brain abnormalities. In an MRI image of the brain, this process involves distinguishing the tissue that makes up the brain from the tissue that does not make up the brain. Even for experienced radiologists, separating the brain from the skull is a difficult task, and the accuracy of the results can vary quite a little from one individual to the next. Therefore, skull stripping in brain magnetic resonance volume has become increasingly popular due to the requirement for a dependable, accurate, and thorough method for processing brain datasets. Furthermore, skull stripping must be performed accurately for neuroimaging diagnostic systems since neither non-brain tissues nor the removal of brain sections can be addressed in the subsequent steps, resulting in an unfixed mistake during further analysis. Therefore, accurate skull stripping is necessary for neuroimaging diagnostic systems. This paper proposes a system based on deep learning and Image processing, an innovative method for converting a pre-trained model into another type of pre-trainer using pre-processing operations and the CLAHE filter as a critical phase. The global IBSR data set was used as a test and training set. For the system's efficacy, work was performed based on the principle of three dimensions and three sections of MR images and two-dimensional images, and the results were 99.9% accurate.

show abstract