RETRACTED: Deep learning for real-time semantic segmentation: Application in ultrasound imaging

Ouahabi, Abdeldjalil; Taleb‐Ahmed, Abdelmalik

doi:10.1016/j.patrec.2021.01.010

Cited by 93 publications

(48 citation statements)

References 29 publications

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“…In particular, when there is no prior knowledge about the relationship between input data and the outcomes to be predicted. Since the pathology imaging tasks are very complex and little is known about which quantitative image features predict the outcomes, deep learning methods are suitable for these tasks [ 21 , 22 ]. In this section, we will describe the state-of-the-art works that have addressed multi-class CRC tissue types and used supervized methods.…”

Section: Related Workmentioning

confidence: 99%

Two Ensemble-CNN Approaches for Colorectal Cancer Tissue Type Classification

et al. 2021

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In recent years, automatic tissue phenotyping has attracted increasing interest in the Digital Pathology (DP) field. For Colorectal Cancer (CRC), tissue phenotyping can diagnose the cancer and differentiate between different cancer grades. The development of Whole Slide Images (WSIs) has provided the required data for creating automatic tissue phenotyping systems. In this paper, we study different hand-crafted feature-based and deep learning methods using two popular multi-classes CRC-tissue-type databases: Kather-CRC-2016 and CRC-TP. For the hand-crafted features, we use two texture descriptors (LPQ and BSIF) and their combination. In addition, two classifiers are used (SVM and NN) to classify the texture features into distinct CRC tissue types. For the deep learning methods, we evaluate four Convolutional Neural Network (CNN) architectures (ResNet-101, ResNeXt-50, Inception-v3, and DenseNet-161). Moreover, we propose two Ensemble CNN approaches: Mean-Ensemble-CNN and NN-Ensemble-CNN. The experimental results show that the proposed approaches outperformed the hand-crafted feature-based methods, CNN architectures and the state-of-the-art methods in both databases.

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Section: Related Workmentioning

confidence: 99%

Two Ensemble-CNN Approaches for Colorectal Cancer Tissue Type Classification

et al. 2021

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“…These final probabilities are calculated by the last layer and the loss function calculate the classification error. The best performing loss function is based on cross-entropy [47], and this is what we have adopted in this work. The optimization of this loss function by stochastic gradient descent [48] allows the learning of the weights in the different layers by backpropagation of the gradient.…”

Section: Properties Of Input Datamentioning

confidence: 99%

Concrete Cracks Detection and Monitoring Using Deep Learning-Based Multiresolution Analysis

et al. 2021

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In this paper, we propose a new methodology for crack detection and monitoring in concrete structures. This approach is based on a multiresolution analysis of a sample or a specimen of concrete material subjected to several types of solicitation. The image obtained by ultrasonic investigation and processed by a customized wavelet is analyzed at various scales in order to detect internal cracks and crack initiation. The ultimate objective of this work is to propose an automatic crack type identification scheme based on convolutional neural networks (CNN). In this context, crack propagation can be monitored without access to the concrete surface and the goal is to detect cracks before they are visible. This is achieved through the combination of two major data analysis tools which are wavelets and deep learning. This original procedure is shown to yield a high accuracy close to 90%. In order to evaluate the performance of the proposed CNN architectures, we also used an open access database, SDNET2018, for the automatic detection of external cracks.

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“…Deep learning methods, which use consecutive hidden layers of information processing organized in a hierarchical manner, have become essential for representation, learning and classification. Considered today in the Top 10 of the most efficient and flexible deep learning techniques, convolutional neural networks (CNNs) are particularly well suited for tasks such as image recognition, image analysis, image segmentation, video analysis or natural language processing [51,52]. However, this type of machine learning requires the use of sufficiently large input database for training and testing to ensure the highest possible accuracy of the recognition process [53].…”

Section: Detecting Cracks In Concrete Using Deep Neural Networkmentioning

confidence: 99%

Wavelet-based multiresolution analysis coupled with deep learning to efficiently monitor cracks in concrete

Arbaoui

Ouahabi

Jacques

et al. 2021

Frattura Ed Integrità Strutturale

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This paper proposes an efficient methodology to monitor the formation of cracks in concrete after non-destructive ultrasonic testing of a structure. The objective is to be able to automatically detect the initiation of cracks early enough, i.e. well before they are visible on the concrete surface, in order to implement adequate maintenance actions on civil engineering structures. The key element of this original approach is the wavelet-based multiresolution analysis of the ultrasonic signal received from a sample or a specimen of the studied material subjected to several types of solicitation. This analysis is finally coupled to an automatic identification scheme of the types of cracks based on artificial neural networks (ANNs), and in particular deep learning by convolutional neural networks (CNNs); a technology today at the cutting edge of machine learning, in particular for all applications of pattern recognition. Wavelet-based multiresolution analysis does not add any value in detecting fractures in concrete visible by optical inspection. However, the results of its implementation coupled with different CNN architectures show cracks in concrete can be identified at an early stage with a very high accuracy, i.e. around 99.8%, and a loss function of less than 0.1, regardless of the implemented learning architecture.

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RETRACTED: Deep learning for real-time semantic segmentation: Application in ultrasound imaging

Cited by 93 publications

References 29 publications

Two Ensemble-CNN Approaches for Colorectal Cancer Tissue Type Classification

Two Ensemble-CNN Approaches for Colorectal Cancer Tissue Type Classification

Concrete Cracks Detection and Monitoring Using Deep Learning-Based Multiresolution Analysis

Wavelet-based multiresolution analysis coupled with deep learning to efficiently monitor cracks in concrete

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