Very deep recurrent convolutional neural network for object recognition

Brahimi, Sourour; Aoun, Najib Ben; Amar, Chokri Ben

doi:10.1117/12.2268672

Cited by 17 publications

(7 citation statements)

References 4 publications

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“…Over the past several years, deep learning techniques have achieved superior performance accuracy over classical machine learning methods on a wide variety of applications, especially in the medical field [10][11][12]. Convolutional Neural Networks (CNNs) [13] are the most common deep architecture used for breast cancer detection and classification.…”

Section: Literature Reviewmentioning

confidence: 99%

“…e segmentation techniques play an essential role in the diagnosis, feature extraction, and classification accuracy of breast masses as benign and malignant. Different deep learning segmentation methods are used for breast cancer images such as FCN [8], U-Net [9,26,27], Segmentation Network (SegNet) [28], Full Resolution Convolutional Network (FrCN) [29], mask Region-Based Convolutional Neural Networks mask (RCNNs) [11,30], Attention guided dense up-sampling network(Aunet) [31], Residual attention U-Net model (RUNet) [32], conditional Generative Adversarial Networks (cGANs) [33], Densely connected U-Net and attention gates (AGs) [34], and Conditional random field model (CRF) [35].…”

Section: Literature Reviewmentioning

confidence: 99%

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Breast Cancer Classification Using FCN and Beta Wavelet Autoencoder

AlEisa

Touiti

Alhussan

et al. 2022

Computational Intelligence and Neuroscience

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In this paper, a new classification approach of breast cancer based on Fully Convolutional Networks (FCNs) and Beta Wavelet Autoencoder (BWAE) is presented. FCN, as a powerful image segmentation model, is used to extract the relevant information from mammography images. It will identify the relevant zones to model while WAE is used to model the extracted information for these zones. In fact, WAE has proven its superiority to the majority of the features extraction approaches. The fusion of these two techniques have improved the feature extraction phase and this by keeping and modeling only the relevant and useful features for the identification and description of breast masses. The experimental results showed the effectiveness of our proposed method which has given very encouraging results in comparison with the states of the art approaches on the same mammographic image base. A precision rate of 94% for benign and 93% for malignant was achieved with a recall rate of 92% for benign and 95% for malignant. For the normal case, we were able to reach a rate of 100%.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Breast Cancer Classification Using FCN and Beta Wavelet Autoencoder

AlEisa

Touiti

Alhussan

et al. 2022

Computational Intelligence and Neuroscience

Self Cite

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“…A proper systematic process needs to analyze the relationship between frustrations, severity, and the adverse influence on students' future performance. e previous studies have various models that can be replicated to optimize the existing prediction systems [23][24][25][26][27][28][29]. Moreover, the instructor usually overcomes students' frustration via collaborative assignments and class activities to provide the best opportunities for learning [30].…”

Section: Literaturementioning

confidence: 99%

Optimization of Students’ Performance Prediction through an Iterative Model of Frustration Severity

Ahmad

Aoun

ElAffendi

et al. 2022

Computational Intelligence and Neuroscience

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Recent articles reported a massive increase in frustration among weak students due to the outbreak of COVID-19 and Massive Open Online Courses (MOOCs). These students need to be evaluated to detect possible psychological counseling and extra attention. On the one hand, the literature reports many optimization techniques focusing on existing students’ performance prediction systems. On the other hand, psychological works provide insights into massive research findings focusing on various students’ emotions, including frustration. However, the synchronization among these contributions is still a black box, which delays the mathematical modeling of students’ frustration. Also, the literature is still limited in using insights of psychology and assumption-based datasets to provide an in-house iterative procedure for modeling students’ frustration severity. This paper proposes an optimization technique called the iterative model of frustration severity (IMFS) to explore the black box. It analyzes students’ performance via two modules. First, frustration is divided into four outer layers. Second, the students’ performance outcome is split into 34 inner layers. The prediction results are iteratively optimized under the umbrella of frustration severity layers through the outer and inner iterations. During validation, the IMFS achieves promising results with various evaluation measures.

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“…Due to the importance of the semantic segmentation, different methods have been developed such as: Graph based methods [Pou15a,Zha14a], Sparse Coding based methods [Zou12a] and CNN based methods [Lon15a,Bad15a,Jég17a,Wu16a]. In this section, we will focus our study on the CNN based methods [Lon15a,Bad15a,Jég17a,Wu16a,Bra16a] since they have shown their good performance and given the best segmentation and recognition results in recent works. The powerful of each CNN variant depends on the network architecture which makes two categories.…”

Section: Related Workmentioning

confidence: 99%

Multiscale Fully Convolutional DenseNet for Semantic Segmentation

Brahimi¹,

Aoun²,

Amar³

et al. 2018

JWSCG

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In the computer vision field, semantic segmentation represents a very interesting task. Convolutional Neural Network methods have shown their great performances in comparison with other semantic segmentation methods. In this paper, we propose a multiscale fully convolutional DenseNet approach for semantic segmentation. Our approach is based on the successful fully convolutional DenseNet method. It is reinforced by integrating a multiscale kernel prediction after the last dense block which performs model averaging over different spatial scales and provides more flexibility of our network to presume more information. Experiments on two semantic segmentation benchmarks: CamVid and Cityscapes have shown the effectiveness of our approach which has outperformed many recent works.

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Very deep recurrent convolutional neural network for object recognition

Cited by 17 publications

References 4 publications

Breast Cancer Classification Using FCN and Beta Wavelet Autoencoder

Breast Cancer Classification Using FCN and Beta Wavelet Autoencoder

Optimization of Students’ Performance Prediction through an Iterative Model of Frustration Severity

Multiscale Fully Convolutional DenseNet for Semantic Segmentation

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