Hybrid between Ontology and Quantum Particle Swarm Optimization for Segmenting Noisy Plant Disease Image

Elsayed, Eman K.; Aly, Mohammed

doi:10.22266/ijies2019.1031.30

Cited by 3 publications

(4 citation statements)

References 33 publications

(61 reference statements)

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“…The method employed an Intel ® i5-2430M CPU @ 2.40 GHz and 8 GB RAM as hardware specifications. Matlab utilized machine learning methods to classify the images and also included built-in medical image segmentation algorithms [34].…”

Section: Toolmentioning

confidence: 99%

EMU-Net: Automatic Brain Tumor Segmentation and Classification Using Efficient Modified U-Net

Aly,

Alotaibi

2023

CMC

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Tumor segmentation is a valuable tool for gaining insights into tumors and improving treatment outcomes. Manual segmentation is crucial but time-consuming. Deep learning methods have emerged as key players in automating brain tumor segmentation. In this paper, we propose an efficient modified U-Net architecture, called EMU-Net, which is applied to the BraTS 2020 dataset. Our approach is organized into two distinct phases: classification and segmentation. In this study, our proposed approach encompasses the utilization of the gray-level co-occurrence matrix (GLCM) as the feature extraction algorithm, convolutional neural networks (CNNs) as the classification algorithm, and the chi-square method for feature selection. Through simulation results, the chi-square method for feature selection successfully identifies and selects four GLCM features. By utilizing the modified U-Net architecture, we achieve precise segmentation of tumor images into three distinct regions: the whole tumor (WT), tumor core (TC), and enhanced tumor (ET). The proposed method consists of two important elements: an encoder component responsible for down-sampling and a decoder component responsible for up-sampling. These components are based on a modified U-Net architecture and are connected by a bridge section. Our proposed CNN architecture achieves superior classification accuracy compared to existing methods, reaching up to 99.65%. Additionally, our suggested technique yields impressive Dice scores of 0.8927, 0.9405, and 0.8487 for the tumor core, whole tumor, and enhanced tumor, respectively. Ultimately, the method presented demonstrates a higher level of trustworthiness and accuracy compared to existing methods. The promising accuracy of the EMU-Net study encourages further testing and evaluation in terms of extrapolation and generalization.

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Section: Toolmentioning

confidence: 99%

EMU-Net: Automatic Brain Tumor Segmentation and Classification Using Efficient Modified U-Net

Aly,

Alotaibi

2023

CMC

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“…On the other side, Ontology is used in expanding semantic knowledge of the concepts in the image processing domain such as the static image of sign language translation domain [5] and image semantic segmentation [23].…”

Section: Convolutionalmentioning

confidence: 99%

Semantic Deep Learning to Translate Dynamic Sign Language

Elsayed¹,

Fathy²

2021

IJIES

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Dynamic Sign Language Recognition aims to recognize hand gestures of any person. Dynamic Sign Language Recognition systems have challenges in recognizing the semantic of hand gestures. These challenges come from the personal differences in hand signs from one person to another. Real-life video gesture frames couldn’t be treated as frame-level as a static sign. This research proposes a semantic translation system for dynamic hand gestures using deep learning and ontology. We used the proposed MSLO (Multi Sign Language Ontology) in the semantic translation step. Also, any user can retrain the system to be a personal one. We used Three-dimensional Convolutional Neural Networks followed by Convolutional long short-term memory to improve the recognition accuracy in Dynamic sign language recognition. We applied the proposed system on three dynamic gesture datasets from color videos. The recognition accuracy average was 97.4%. We did all the training and testing processes on the Graphics Processing Unit with the support of Google Colab. Using "Google Colab" in the training process decreases the average run time by about 87.9%. In addition to adding semantic in dynamic sign language translation, the proposed system achieves good results compared to some dynamic sign language recognition systems.

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“…Every internal iteration of the initial algorithm includes the following. The decomposition of the blurry image into inconsistent patches, the sparse coding of image patches, the calculation of sparse coefficients, and the reconstruction of the noisy image from the estimated patches [2,3]. In such modules, the dictionary operates a significant part.…”

Section: Fixed or Pre-learned Dictionarymentioning

confidence: 99%

“…The primary purpose of the denoising image is to eliminate noise from corrupted images in an attempt to approximate their original image. This is to ensure that during maintenance the edges, textures, and information of the respective features remain consistent [2]. In recent years, numerous image denoising algorithms have been proposed and successfully implemented.…”

Section: Introductionmentioning

confidence: 99%

A Novel Method to Denoise Images Based on a Meta-heuristic Algorithm and Pre-learned Dictionary

Alotaibi¹

2021

IJIES

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Noise may affect images in many ways during different processes. Such as during obtaining, distribution, processing, or compressing. The Sparse Representation (SR) algorithm is one of the best strategies for noise reduction. One meta-heuristic algorithm is the Particle Swarm Optimization (PSO). This research demonstrates excellent results in noise reduction in the Fast PSO version while utilizing the SRs as well as meta-heuristic algorithms to gain. This method is known as FPSO-MP and it depends on the Pursuit Algorithm (MP) that matches. In this proposed study, a Dynamic-Multi-Swarm (DMS) method and a pre-learned dictionary (FPSO-MP) approach is presented to reduce the time for the learning dictionary calculations. The output of the denoising algorithm QPSO-MP is dependable on dictionary learning because of the dictionary size or increased number of patches. Similar to this work, a Non-locally Estimated Sparse Coefficient (NESC) is one explanation for the low efficiency of the original algorithm. Compared to the original PSO-MP method, these enhancements have achieved substantial gains in computational efficiency of approximately 92% without sacrosanct image quality. After modification, the proposed FPSO-MP technique is in contrast with the original PSO-MP method. The scientific findings demonstrate that the FPSO-MP algorithm is much more efficient and faster than the original algorithm, without affecting image quality. The proposed method follows the original technique and therefore reduces during run-time. The result of this study demonstrates that the bestdenoised images can always be accessed from the pre-learned dictionary rather than the learning dictionary developed across the noisy image during runtime. We constructed images dataset from the BSD500 collection and performed a statistical test on these images. The actual findings reveal that the suggested method is excellent for noise reduction (noise elimination) as well as highly efficient during runtime. The analytical findings indicate that both quantitative and image performance outcomes are obtained with the proposed FPSO-MP approach during its contradiction with when denoising algorithms.

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Hybrid between Ontology and Quantum Particle Swarm Optimization for Segmenting Noisy Plant Disease Image

Cited by 3 publications

References 33 publications

EMU-Net: Automatic Brain Tumor Segmentation and Classification Using Efficient Modified U-Net

EMU-Net: Automatic Brain Tumor Segmentation and Classification Using Efficient Modified U-Net

Semantic Deep Learning to Translate Dynamic Sign Language

A Novel Method to Denoise Images Based on a Meta-heuristic Algorithm and Pre-learned Dictionary

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